Package evaluation of TextClassification on Julia 1.10.9 (96dc2d8c45*) started at 2025-06-06T20:26:36.545 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 5.33s ################################################################################ # Installation # Installing TextClassification... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [8e067cb0] + TextClassification v0.6.1 Updating `~/.julia/environments/v1.10/Manifest.toml` [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.19.0 [a9b6321e] + Atomix v1.1.1 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.6 [d360d2e6] + ChainRulesCore v1.25.1 [fb6a15b2] + CloseOpenIntervals v0.1.13 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.5.8 [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 v0.9.4 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.17.0 [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 v0.2.4 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] + JLD2 v0.4.54 [692b3bcd] + JLLWrappers v1.7.0 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.34 [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 v0.9.30 [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 v1.2.1 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [92933f4c] + ProgressMeter v1.10.4 [ae029012] + Requires v1.3.1 [94e857df] + SIMDTypes v0.1.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [7e506255] + ScopedValues v1.3.0 [0e966ebe] + SearchModels v0.4.1 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [053f045d] + SimilaritySearch v0.12.0 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 [171d559e] + SplittablesBase v0.1.15 [aedffcd0] + Static v1.2.0 [0d7ed370] + StaticArrayInterface v1.8.0 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [82ae8749] + StatsAPI v1.7.1 ⌅ [2913bbd2] + StatsBase v0.33.21 [7792a7ef] + StrideArraysCore v0.5.7 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [8e067cb0] + TextClassification v0.6.1 [7f6f6c8a] + TextSearch v0.19.5 [8290d209] + ThreadingUtilities v0.5.4 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.84 [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 v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 8.81s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 62.18s ################################################################################ # Testing # Testing TextClassification Status `/tmp/jl_hRPVLO/Project.toml` [4c88cf16] Aqua v0.8.13 [336ed68f] CSV v0.10.15 [944b1d66] CodecZlib v0.7.8 [b20bd276] InvertedFiles v0.8.0 [c8e1da08] IterTools v1.10.0 [682c06a0] JSON v0.21.4 [b1bec4e5] LIBSVM v0.8.1 [f1d291b0] MLUtils v0.4.8 [d96e819e] Parameters v0.12.3 [0e966ebe] SearchModels v0.4.1 [053f045d] SimilaritySearch v0.12.0 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 [8e067cb0] TextClassification v0.6.1 [7f6f6c8a] TextSearch v0.19.5 [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [37e2e46d] LinearAlgebra [9a3f8284] Random [2f01184e] SparseArrays v1.10.0 [8dfed614] Test Status `/tmp/jl_hRPVLO/Manifest.toml` [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [4c88cf16] Aqua v0.8.13 [dce04be8] ArgCheck v2.5.0 [4fba245c] ArrayInterface v7.19.0 [a9b6321e] Atomix v1.1.1 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.6 [336ed68f] CSV v0.10.15 [d360d2e6] ChainRulesCore v1.25.1 [fb6a15b2] CloseOpenIntervals v0.1.13 [944b1d66] CodecZlib v0.7.8 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.16.0 [a33af91c] CompositionsBase v0.1.2 [187b0558] ConstructionBase v1.5.8 [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 v0.9.4 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [5789e2e9] FileIO v1.17.0 [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 v1.4.3 [6d0fbc77] Intersections v0.4.0 [3587e190] InverseFunctions v0.1.17 [b20bd276] InvertedFiles v0.8.0 [92d709cd] IrrationalConstants v0.2.4 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.4.54 [692b3bcd] JLLWrappers v1.7.0 [682c06a0] JSON v0.21.4 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.34 [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 v0.9.30 [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 v1.2.1 [21216c6a] Preferences v1.4.3 [8162dcfd] PrettyPrint v0.2.0 [92933f4c] ProgressMeter v1.10.4 [ae029012] Requires v1.3.1 [94e857df] SIMDTypes v0.1.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.3.0 [0e966ebe] SearchModels v0.4.1 [91c51154] SentinelArrays v1.4.8 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [053f045d] SimilaritySearch v0.12.0 [699a6c99] SimpleTraits v0.9.4 [a2af1166] SortingAlgorithms v1.2.1 [171d559e] SplittablesBase v0.1.15 [aedffcd0] Static v1.2.0 [0d7ed370] StaticArrayInterface v1.8.0 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 [7792a7ef] StrideArraysCore v0.5.7 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [8e067cb0] TextClassification v0.6.1 [7f6f6c8a] TextSearch v0.19.5 [8290d209] ThreadingUtilities v0.5.4 [3bb67fe8] TranscodingStreams v0.11.3 [28d57a85] Transducers v0.4.84 [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 v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [ade2ca70] Dates [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them 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}("💔" => 551, "♡" => 542) countmap(testlabels) = Dict{Any, Int64}("💔" => 232, "♡" => 237) 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 = -236.949052, rho = 0.234653 nSV = 425, nBSV = 232 Total nSV = 425 *.* optimization finished, #iter = 868 obj = -322.668093, rho = 0.321558 nSV = 482, nBSV = 339 Total nSV = 482 *.* optimization finished, #iter = 872 obj = -238.648069, rho = 0.136038 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 = -201.887441, 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 = 0.212288 nSV = 504, nBSV = 313 Total nSV = 504 .* optimization finished, #iter = 1000 obj = -291.121494, rho = 0.213544 nSV = 527, nBSV = 293 Total nSV = 527 *.* optimization finished, #iter = 804 obj = -285.108996, 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 = -316.877834, rho = 0.258822 nSV = 494, nBSV = 330 Total nSV = 494 *.* optimization finished, #iter = 997 obj = -266.518388, rho = 0.058243 nSV = 412, nBSV = 275 Total nSV = 412 *.* optimization finished, #iter = 960 obj = -303.631753, rho = 0.266331 nSV = 525, nBSV = 310 Total nSV = 525 *.* optimization finished, #iter = 910 obj = -223.003637, 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 = 1147 obj = -199.560911, rho = 0.240666 nSV = 450, nBSV = 182 Total nSV = 450 *.* optimization finished, #iter = 925 obj = -314.355479, rho = 0.286201 nSV = 496, nBSV = 323 Total nSV = 496 .* optimization finished, #iter = 1209 obj = -180.245889, rho = 0.166523 nSV = 464, nBSV = 141 Total nSV = 464 *.* optimization finished, #iter = 1044 obj = -203.353303, 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 = -246.443789, 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 = -285.378341, rho = 0.152429 nSV = 546, nBSV = 283 Total nSV = 546 .* optimization finished, #iter = 1072 obj = -302.478813, 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 = -291.603428, 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 = -144.013542, rho = 0.193111 nSV = 629, nBSV = 500 Total nSV = 629 * optimization finished, #iter = 694 obj = -118.070464, rho = 0.250956 nSV = 472, nBSV = 387 Total nSV = 472 .*.* optimization finished, #iter = 1826 obj = -601.993461, 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) 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#61 @ ~/.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"#76#79"{TextClassification.var"#77#80", 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"#76#79"{TextClassification.var"#77#80", 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"#17#23", sort_by_best::typeof(SearchModels.sort_by_best), convergence::SearchModels.var"#18#24", 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"#77#80", 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] macro expansion @ ~/.julia/packages/TextClassification/q1c16/test/runtests.jl:32 [inlined] [16] macro expansion @ /opt/julia/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined] [17] top-level scope @ ~/.julia/packages/TextClassification/q1c16/test/runtests.jl:7 [18] include(fname::String) @ Base.MainInclude ./client.jl:494 [19] top-level scope @ none:6 [20] eval @ ./boot.jl:385 [inlined] [21] exec_options(opts::Base.JLOptions) @ Base ./client.jl:296 [22] _start() @ Base ./client.jl:557[ 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: 6, observed: 46, best-error: 0.208779761904762 worst-error: 0.2266369047619048 ┌ 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.FreqWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1121 obj = -332.984278, rho = 0.340831 nSV = 488, nBSV = 337 Total nSV = 488 ┌ 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 1.3 │ min_token_ndocs: Int64 9 │ max_token_pdocs: Float64 0.6 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .*.* optimization finished, #iter = 1787 obj = -1022.271822, rho = 0.348938 nSV = 404, nBSV = 321 Total nSV = 404 .*.* optimization finished, #iter = 2084 obj = -663.082183, rho = -0.000944 nSV = 377, nBSV = 194 Total nSV = 377 * optimization finished, #iter = 747 obj = -146.331825, rho = 0.249293 nSV = 641, nBSV = 520 Total nSV = 641 .*.* optimization finished, #iter = 2098 obj = -663.082189, rho = -0.001009 nSV = 377, nBSV = 194 Total nSV = 377 .*.* optimization finished, #iter = 1849 obj = -395.312892, rho = 0.124623 nSV = 526, nBSV = 44 Total nSV = 526 SearchModels iteration 3> population: 8, bsize: 2, queue: 8, observed: 54, best-error: 0.208779761904762 worst-error: 0.2260416666666667 .* optimization finished, #iter = 1050 obj = -286.068115, rho = 0.139148 nSV = 549, nBSV = 280 Total nSV = 549 * optimization finished, #iter = 644 obj = -121.467589, rho = 0.100298 nSV = 482, nBSV = 400 Total nSV = 482 * optimization finished, #iter = 698 obj = -121.769082, rho = 0.177094 nSV = 485, nBSV = 396 Total nSV = 485 .*.* optimization finished, #iter = 1926 obj = -435.985584, rho = 0.179790 nSV = 495, nBSV = 75 Total nSV = 495 * optimization finished, #iter = 712 obj = -143.224923, rho = 0.219616 nSV = 606, nBSV = 499 Total nSV = 606 *.* optimization finished, #iter = 797 obj = -281.925943, rho = 0.045801 nSV = 407, nBSV = 291 Total nSV = 407 .*.* optimization finished, #iter = 1993 obj = -435.407458, rho = 0.212916 nSV = 480, nBSV = 81 Total nSV = 480 .*.* optimization finished, #iter = 1983 obj = -437.487729, rho = 0.186444 nSV = 496, nBSV = 75 Total nSV = 496 SearchModels iteration 4> population: 8, bsize: 2, queue: 6, observed: 60, best-error: 0.208779761904762 worst-error: 0.22291666666666665 * optimization finished, #iter = 683 obj = -119.734706, rho = 0.152869 nSV = 485, nBSV = 401 Total nSV = 485 .* optimization finished, #iter = 1036 obj = -288.085525, rho = 0.110785 nSV = 545, nBSV = 284 Total nSV = 545 ┌ Warning: using 763 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[TextSearch.Skipgram(2, 1)], 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 } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .**.* optimization finished, #iter = 1566 obj = -984.689854, rho = 0.081565 nSV = 403, nBSV = 303 Total nSV = 403 .*.* optimization finished, #iter = 2112 obj = -546.647478, rho = 0.055297 nSV = 379, nBSV = 155 Total nSV = 379 .*.* optimization finished, #iter = 1913 obj = -571.209195, rho = -0.080446 nSV = 369, nBSV = 158 Total nSV = 369 ┌ 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 iteration 5> population: 8, bsize: 2, queue: 11, observed: 71, best-error: 0.208779761904762 worst-error: 0.22276785714285707 .* optimization finished, #iter = 1056 obj = -275.766126, rho = 0.153214 nSV = 562, nBSV = 267 Total nSV = 562 ┌ Warning: using 116 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(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 52 obj = -10.279148, rho = 0.161853 nSV = 52, nBSV = 43 Total nSV = 52 * optimization finished, #iter = 693 obj = -141.130278, rho = 0.362470 nSV = 567, nBSV = 475 Total nSV = 567 *.* optimization finished, #iter = 854 obj = -316.208949, rho = 0.223767 nSV = 496, nBSV = 330 Total nSV = 496 * optimization finished, #iter = 701 obj = -140.652483, rho = 0.245413 nSV = 562, nBSV = 468 Total nSV = 562 * optimization finished, #iter = 727 obj = -145.420784, rho = 0.195065 nSV = 621, nBSV = 506 Total nSV = 621 .*.* optimization finished, #iter = 1991 obj = -448.250999, rho = 0.164773 nSV = 496, nBSV = 83 Total nSV = 496 * optimization finished, #iter = 735 obj = -144.109952, rho = 0.227491 nSV = 616, nBSV = 506 Total nSV = 616 [ 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.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()))[ 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[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))[ 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[], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()))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=6 (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[], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.65 └ LIBSVMConfig }, 0.20892857142857146) ┌ 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 1.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance └ LIBSVMConfig }, 0.21160714285714288) ┌ 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.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[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.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[7]:", 7, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.2209821428571428) ┌ 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}("💔" => 0.7991886409736308, "♡" => 0.7775280898876404), classprecision = Dict{Any, Any}("💔" => 0.7547892720306514, "♡" => 0.8317307692307693), classrecall = Dict{Any, Any}("💔" => 0.8491379310344828, "♡" => 0.729957805907173)) SearchModels> search params iter=0, initialpopulation=32, maxpopulation=8, bsize=2, mutbsize=8, crossbsize=8 .* optimization finished, #iter = 981 obj = -310.449935, rho = 0.181957 nSV = 462, nBSV = 333 Total nSV = 462 .* optimization finished, #iter = 991 obj = -268.528312, rho = 0.168428 nSV = 516, nBSV = 275 Total nSV = 516 *.* optimization finished, #iter = 964 obj = -294.274589, rho = 0.251659 nSV = 469, nBSV = 307 Total nSV = 469 *.* optimization finished, #iter = 836 obj = -288.336956, rho = 0.158727 nSV = 464, nBSV = 307 Total nSV = 464 vectorizing corpus 0%|▏ | ETA: 0:06:18vectorizing corpus 0%|▏ | ETA: 0:12:16vectorizing corpus 0%|▏ | ETA: 0:12:53vectorizing corpus 0%|▏ | ETA: 0:12:53vectorizing corpus 0%|▏ | ETA: 0:12:53vectorizing corpus 0%|▏ | ETA: 0:12:53*.* optimization finished, #iter = 976 obj = -257.003905, rho = 0.094316 nSV = 416, nBSV = 271 Total nSV = 416 *.* optimization finished, #iter = 779 obj = -255.946312, rho = 0.197089 nSV = 393, nBSV = 271 Total nSV = 393 *.* optimization finished, #iter = 781 obj = -231.198507, rho = 0.141579 nSV = 381, nBSV = 251 Total nSV = 381 *.* optimization finished, #iter = 861 obj = -191.339472, rho = 0.037734 nSV = 411, nBSV = 189 Total nSV = 411 *.* optimization finished, #iter = 791 obj = -253.787510, rho = 0.148708 nSV = 390, nBSV = 261 Total nSV = 390 *.* optimization finished, #iter = 874 obj = -235.803181, rho = 0.279253 nSV = 412, nBSV = 248 Total nSV = 412 *.* optimization finished, #iter = 732 obj = -256.082465, rho = 0.124869 nSV = 395, nBSV = 272 Total nSV = 395 vectorizing corpus 0%|▏ | ETA: 0:13:47 vectorizing corpus 41%|█████████████▋ | ETA: 0:00:01vectorizing corpus 55%|██████████████████▎ | ETA: 0:00:01vectorizing corpus 53%|█████████████████▋ | ETA: 0:00:01vectorizing corpus 41%|█████████████▍ | ETA: 0:00:01vectorizing corpus 87%|████████████████████████████▌ | ETA: 0:00:00vectorizing corpus 0%|▏ | ETA: 0:06:06vectorizing corpus 32%|██████████▋ | ETA: 0:00:02vectorizing corpus 23%|███████▌ | ETA: 0:00:03vectorizing corpus 20%|██████▊ | ETA: 0:00:04vectorizing corpus 24%|███████▊ | ETA: 0:00:03vectorizing corpus 29%|█████████▌ | ETA: 0:00:02vectorizing corpus 28%|█████████▎ | ETA: 0:00:03vectorizing corpus 35%|███████████▌ | ETA: 0:00:02 vectorizing corpus 1%|▏ | ETA: 0:04:12vectorizing corpus 1%|▏ | ETA: 0:03:50*.* optimization finished, #iter = 913 obj = -301.143738, rho = -0.213819 nSV = 453, nBSV = 317 Total nSV = 453 *.* optimization finished, #iter = 951 obj = -278.097480, rho = -0.054763 nSV = 537, nBSV = 287 Total nSV = 537 *.* optimization finished, #iter = 948 obj = -294.578517, rho = -0.210231 nSV = 465, nBSV = 310 Total nSV = 465 *.* optimization finished, #iter = 901 obj = -293.197003, rho = -0.159235 nSV = 475, nBSV = 317 Total nSV = 475  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: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08 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: 0:00:06vectorizing 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: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05 *.* optimization finished, #iter = 821 obj = -230.583650, rho = -0.121530 nSV = 391, nBSV = 246 Total nSV = 391 .* optimization finished, #iter = 1237 obj = -205.796054, rho = 0.024755 nSV = 439, nBSV = 176 Total nSV = 439 *.* optimization finished, #iter = 913 obj = -263.393642, rho = -0.162936 nSV = 402, nBSV = 268 Total nSV = 402 *.* optimization finished, #iter = 932 obj = -238.799482, rho = -0.184136 nSV = 416, nBSV = 247 Total nSV = 416 *.* optimization finished, #iter = 915 obj = -258.932376, rho = -0.053509 nSV = 407, nBSV = 265 Total nSV = 407 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05 *.* optimization finished, #iter = 893 obj = -305.870487, rho = -0.295291 nSV = 451, nBSV = 318 Total nSV = 451 .* optimization finished, #iter = 974 obj = -271.896733, rho = -0.180442 nSV = 520, nBSV = 268 Total nSV = 520 *.* optimization finished, #iter = 1013 obj = -299.117572, rho = -0.251055 nSV = 473, nBSV = 316 Total nSV = 473 *.* optimization finished, #iter = 883 obj = -288.469311, rho = -0.211003 nSV = 464, nBSV = 297 Total nSV = 464 *.* optimization finished, #iter = 864 obj = -268.536780, rho = 0.168143 nSV = 491, nBSV = 276 Total nSV = 491 *.* optimization finished, #iter = 835 obj = -286.525134, rho = 0.179804 nSV = 504, nBSV = 302 Total nSV = 504 *.* optimization finished, #iter = 943 obj = -313.179847, rho = 0.159104 nSV = 443, nBSV = 329 Total nSV = 443 *.* optimization finished, #iter = 967 obj = -278.502281, rho = 0.221027 nSV = 490, nBSV = 286 Total nSV = 490 *.* optimization finished, #iter = 845 obj = -335.506289, rho = 0.216983 nSV = 462, nBSV = 351 Total nSV = 462 .* optimization finished, #iter = 1042 obj = -288.320876, rho = 0.256148 nSV = 473, nBSV = 298 Total nSV = 473 *.* optimization finished, #iter = 806 obj = -311.055320, rho = 0.190246 nSV = 460, nBSV = 343 Total nSV = 460 *.* optimization finished, #iter = 850 obj = -194.204477, rho = -0.023977 nSV = 396, nBSV = 190 Total nSV = 396 *.* optimization finished, #iter = 887 obj = -256.149455, rho = 0.280784 nSV = 401, nBSV = 271 Total nSV = 401 *.* optimization finished, #iter = 801 obj = -255.445074, rho = 0.272951 nSV = 397, nBSV = 271 Total nSV = 397 *.* optimization finished, #iter = 887 obj = -268.473756, rho = 0.078856 nSV = 406, nBSV = 288 Total nSV = 406 *.* optimization finished, #iter = 938 obj = -216.883502, rho = 0.183201 nSV = 421, nBSV = 218 Total nSV = 421 .* optimization finished, #iter = 1186 obj = -166.398837, rho = 0.094984 nSV = 439, nBSV = 123 Total nSV = 439 *.* optimization finished, #iter = 929 obj = -266.778613, rho = 0.030563 nSV = 403, nBSV = 277 Total nSV = 403 *.* optimization finished, #iter = 865 obj = -246.977480, rho = 0.055946 nSV = 412, nBSV = 245 Total nSV = 412 *.* optimization finished, #iter = 795 obj = -275.600763, rho = 0.131191 nSV = 401, nBSV = 287 Total nSV = 401 *.* optimization finished, #iter = 929 obj = -239.975784, rho = -0.001110 nSV = 404, nBSV = 245 Total nSV = 404 *.* optimization finished, #iter = 880 obj = -218.965612, rho = 0.191430 nSV = 405, nBSV = 227 Total nSV = 405 *.* optimization finished, #iter = 954 obj = -187.712223, rho = 0.099219 nSV = 394, nBSV = 183 Total nSV = 394 *.* optimization finished, #iter = 800 obj = -242.683140, rho = 0.148538 nSV = 391, nBSV = 263 Total nSV = 391 vectorizing corpus 1%|▏ | ETA: 0:03:16vectorizing corpus 1%|▏ | ETA: 0:03:06 vectorizing corpus 1%|▏ | ETA: 0:08:16vectorizing corpus 1%|▏ | ETA: 0:07:53vectorizing corpus 1%|▏ | ETA: 0:07:25vectorizing corpus 1%|▏ | ETA: 0:06:55vectorizing corpus 1%|▏ | ETA: 0:06:18vectorizing corpus 1%|▏ | ETA: 0:06:00vectorizing corpus 1%|▏ | ETA: 0:05:43vectorizing corpus 1%|▏ | ETA: 0:05:27vectorizing corpus 1%|▏ | ETA: 0:05:07vectorizing corpus 1%|▏ | ETA: 0:04:38vectorizing corpus 1%|▏ | ETA: 0:04:06vectorizing corpus 1%|▏ | ETA: 0:03:43*.* optimization finished, #iter = 945 obj = -227.530196, rho = -0.215968 nSV = 391, nBSV = 223 Total nSV = 391 *.* optimization finished, #iter = 972 obj = -197.568040, rho = -0.052846 nSV = 412, nBSV = 178 Total nSV = 412 *.* optimization finished, #iter = 775 obj = -259.203351, rho = -0.086025 nSV = 397, nBSV = 258 Total nSV = 397 *.* optimization finished, #iter = 892 obj = -232.081401, rho = -0.259030 nSV = 412, nBSV = 239 Total nSV = 412 *.* optimization finished, #iter = 982 obj = -253.022497, rho = -0.185861 nSV = 413, nBSV = 250 Total nSV = 413 vectorizing corpus 1%|▏ | ETA: 0:03:26*.* optimization finished, #iter = 887 obj = -279.545778, rho = -0.256571 nSV = 506, nBSV = 293 Total nSV = 506 *.* optimization finished, #iter = 955 obj = -309.237986, rho = -0.236835 nSV = 444, nBSV = 327 Total nSV = 444 .* optimization finished, #iter = 1043 obj = -284.731186, rho = -0.128706 nSV = 509, nBSV = 289 Total nSV = 509 ┌ Warning: using 728 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1005 obj = -299.090847, rho = -0.165246 nSV = 474, nBSV = 303 Total nSV = 474 *.* optimization finished, #iter = 795 obj = -309.060955, rho = -0.250732 nSV = 446, nBSV = 332 Total nSV = 446 *.* optimization finished, #iter = 944 obj = -330.070976, rho = -0.264107 nSV = 461, nBSV = 344 Total nSV = 461 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 1%|▏ | ETA: 0:04:16vectorizing corpus 1%|▏ | ETA: 0:04:02vectorizing corpus 1%|▏ | ETA: 0:03:22vectorizing corpus 1%|▏ | ETA: 0:03:12*.* optimization finished, #iter = 1042 obj = -279.075998, rho = 0.093453 nSV = 431, nBSV = 289 Total nSV = 431 *.* optimization finished, #iter = 831 obj = -252.104267, rho = -0.170628 nSV = 400, nBSV = 266 Total nSV = 400 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08   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: 0:00:06vectorizing 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 = 944 obj = -303.754163, rho = -0.266478 nSV = 456, nBSV = 312 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[5], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 918 obj = -329.212153, rho = -0.389752 nSV = 458, nBSV = 342 Total nSV = 458 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03 vectorizing corpus 0%|▏ | ETA: 0:07:39vectorizing corpus 0%|▏ | ETA: 0:07:33*.* optimization finished, #iter = 976 obj = -264.820053, rho = -0.228213 nSV = 508, nBSV = 271 Total nSV = 508 *.* optimization finished, #iter = 860 obj = -237.664959, rho = -0.157822 nSV = 399, nBSV = 243 Total nSV = 399 *.* optimization finished, #iter = 809 obj = -281.398470, rho = -0.079748 nSV = 404, nBSV = 285 Total nSV = 404 *.* optimization finished, #iter = 1006 obj = -255.314569, rho = 0.100252 nSV = 415, nBSV = 266 Total nSV = 415 *.* optimization finished, #iter = 832 obj = -231.544014, rho = -0.074234 nSV = 417, nBSV = 240 Total nSV = 417 *.* optimization finished, #iter = 942 obj = -208.918343, rho = -0.008462 nSV = 417, nBSV = 204 Total nSV = 417 *.* optimization finished, #iter = 777 obj = -243.067037, rho = -0.112190 nSV = 390, nBSV = 251 Total nSV = 390 vectorizing corpus 0%|▏ | ETA: 0:06:52vectorizing corpus 0%|▏ | ETA: 0:06:47vectorizing corpus 0%|▏ | ETA: 0:06:40vectorizing corpus 0%|▏ | ETA: 0:06:34vectorizing corpus 0%|▏ | ETA: 0:06:27vectorizing corpus 0%|▏ | ETA: 0:06:20vectorizing corpus 0%|▏ | ETA: 0:06:14 *.* optimization finished, #iter = 862 obj = -280.673486, rho = -0.183861 nSV = 487, nBSV = 284 Total nSV = 487 *.* optimization finished, #iter = 943 obj = -311.157300, rho = -0.282495 nSV = 448, nBSV = 320 Total nSV = 448 *.* optimization finished, #iter = 933 obj = -277.582793, rho = -0.212669 nSV = 494, nBSV = 292 Total nSV = 494 *.* optimization finished, #iter = 866 obj = -267.068269, rho = -0.142901 nSV = 499, nBSV = 277 Total nSV = 499 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05.* optimization finished, #iter = 1053 obj = -286.026419, rho = -0.259590 nSV = 479, nBSV = 297 Total nSV = 479  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06*.* optimization finished, #iter = 1040 obj = -248.096893, rho = -0.027507 nSV = 407, nBSV = 240 Total nSV = 407 *.* optimization finished, #iter = 872 obj = -273.194072, rho = -0.123774 nSV = 400, nBSV = 278 Total nSV = 400 *.* optimization finished, #iter = 886 obj = -245.187235, rho = 0.013597 nSV = 394, nBSV = 249 Total nSV = 394 *.* optimization finished, #iter = 940 obj = -220.122446, rho = -0.149348 nSV = 401, nBSV = 208 Total nSV = 401 *.* optimization finished, #iter = 967 obj = -198.852458, rho = -0.073431 nSV = 402, nBSV = 187 Total nSV = 402 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06*.* optimization finished, #iter = 849 obj = -260.784953, rho = -0.025373 nSV = 401, nBSV = 266 Total nSV = 401 *.* optimization finished, #iter = 950 obj = -248.727589, rho = -0.245899 nSV = 399, nBSV = 254 Total nSV = 399 *.* optimization finished, #iter = 890 obj = -238.633444, rho = -0.220694 nSV = 396, nBSV = 239 Total nSV = 396  .* optimization finished, #iter = 1112 obj = -215.690032, rho = 0.068225 nSV = 434, nBSV = 207 Total nSV = 434 *.* optimization finished, #iter = 931 obj = -270.400202, rho = 0.008253 nSV = 411, nBSV = 275 Total nSV = 411 *.* optimization finished, #iter = 874 obj = -263.908592, rho = -0.066039 nSV = 409, nBSV = 279 Total nSV = 409 *.* optimization finished, #iter = 905 obj = -280.477106, rho = 0.038751 nSV = 416, nBSV = 287 Total nSV = 416 *.* optimization finished, #iter = 980 obj = -233.213406, rho = -0.105695 nSV = 430, nBSV = 236 Total nSV = 430 .* optimization finished, #iter = 1131 obj = -169.826278, rho = -0.121849 nSV = 440, nBSV = 130 Total nSV = 440 *.* optimization finished, #iter = 967 obj = -278.634250, rho = 0.049340 nSV = 416, nBSV = 280 Total nSV = 416 *.* optimization finished, #iter = 998 obj = -202.957442, rho = 0.012331 nSV = 408, nBSV = 181 Total nSV = 408 *.* optimization finished, #iter = 922 obj = -260.888554, rho = -0.136926 nSV = 401, nBSV = 273 Total nSV = 401 *.* optimization finished, #iter = 932 obj = -251.687482, rho = -0.127799 nSV = 396, nBSV = 259 Total nSV = 396 *.* optimization finished, #iter = 845 obj = -262.405321, rho = -0.012971 nSV = 394, nBSV = 274 Total nSV = 394 *.* optimization finished, #iter = 929 obj = -218.876158, rho = -0.142090 nSV = 409, nBSV = 209 Total nSV = 409 .* optimization finished, #iter = 1299 obj = -165.228581, rho = -0.068412 nSV = 452, nBSV = 117 Total nSV = 452 .* optimization finished, #iter = 1079 obj = -265.024414, rho = -0.108158 nSV = 406, nBSV = 270 Total nSV = 406 SearchModels iteration 1> population: 8, bsize: 2, queue: 11, observed: 42, best-error: 0.2235043471784991 worst-error: 0.24515901969356557 [ Info: TaskFailedException(Task (failed) @0x00007118390033a0) *.* optimization finished, #iter = 949 obj = -281.489697, rho = 0.219196 nSV = 478, nBSV = 294 Total nSV = 478 .*.* optimization finished, #iter = 2173 obj = -566.862537, rho = 0.232511 nSV = 413, nBSV = 153 Total nSV = 413 [ Info: ignoring configuration due to exception *.* optimization finished, #iter = 914 obj = -262.495511, rho = 0.229135 nSV = 403, nBSV = 280 Total nSV = 403 *.* optimization finished, #iter = 985 obj = -252.815180, rho = 0.255235 nSV = 403, nBSV = 262 Total nSV = 403 * optimization finished, #iter = 605 obj = -115.505311, rho = 0.248550 nSV = 472, nBSV = 403 Total nSV = 472 .*.* optimization finished, #iter = 1658 obj = -409.701480, rho = 0.223833 nSV = 351, nBSV = 104 Total nSV = 351 ┌ Warning: using 0 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3, 5], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.FreqWeighting} │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.0 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 0 obj = 0.000000, rho = nan nSV = 0, nBSV = 0 ┌ Warning: using 0 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.EntModelConfig{TextSearch.FreqWeighting} │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.0 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1053 obj = -278.483280, rho = -0.157937 nSV = 485, nBSV = 284 Total nSV = 485 .*.* optimization finished, #iter = 2075 obj = -571.460080, rho = -0.003830 nSV = 414, nBSV = 160 Total nSV = 414 *.* optimization finished, #iter = 0 obj = 0.000000, rho = nan nSV = 0, nBSV = 0 ┌ Warning: using 105 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 1.5384615384615383 │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.7 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 35 obj = -8.074904, rho = -0.073124 nSV = 32, nBSV = 25 Total nSV = 32 [ Info: TaskFailedException(Task (failed) @0x00007118390036c0) *.* optimization finished, #iter = 863 obj = -277.578200, rho = 0.036867 nSV = 416, nBSV = 289 Total nSV = 416 *.* optimization finished, #iter = 948 obj = -267.077390, rho = 0.001822 nSV = 414, nBSV = 274 Total nSV = 414 * optimization finished, #iter = 625 obj = -117.812374, rho = -0.121048 nSV = 481, nBSV = 392 Total nSV = 481 .*.* optimization finished, #iter = 1827 obj = -435.740459, rho = 0.029712 nSV = 359, nBSV = 117 Total nSV = 359 [ Info: ignoring configuration due to exception .* optimization finished, #iter = 1030 obj = -282.365974, rho = -0.191963 nSV = 489, nBSV = 283 Total nSV = 489 ..**.* optimization finished, #iter = 2152 obj = -589.241018, rho = -0.315392 nSV = 424, nBSV = 163 Total nSV = 424 [ Info: TaskFailedException(Task (failed) @0x0000711839003850) [ Info: ignoring configuration due to exception [ Info: TaskFailedException(Task (failed) @0x00007118390039e0) *.* optimization finished, #iter = 889 obj = -264.773961, rho = -0.106675 nSV = 401, nBSV = 277 Total nSV = 401 *.* optimization finished, #iter = 855 obj = -256.064428, rho = -0.137326 nSV = 397, nBSV = 265 Total nSV = 397 * optimization finished, #iter = 618 obj = -114.166244, rho = -0.178858 nSV = 461, nBSV = 388 Total nSV = 461 .*.* optimization finished, #iter = 1608 obj = -442.428647, rho = -0.112691 nSV = 349, nBSV = 114 Total nSV = 349 [ Info: ignoring configuration due to exception ┌ Warning: using 81 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 1.5384615384615383 │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.7 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 25 obj = -5.286964, rho = 0.154743 nSV = 21, nBSV = 17 Total nSV = 21 ┌ Warning: using 93 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 1.5384615384615383 │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.7 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 29 obj = -5.768131, rho = -0.393230 nSV = 25, nBSV = 18 Total nSV = 25 SearchModels iteration 2> population: 8, bsize: 2, queue: 10, observed: 52, best-error: 0.2235043471784991 worst-error: 0.24011031780046144 *.* optimization finished, #iter = 955 obj = -300.808684, rho = 0.199489 nSV = 479, nBSV = 317 Total nSV = 479 ┌ Warning: using 724 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 10 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 667 obj = -136.077373, rho = 0.376764 nSV = 545, nBSV = 453 Total nSV = 545 .* optimization finished, #iter = 1151 obj = -280.563972, rho = 0.273689 nSV = 491, nBSV = 294 Total nSV = 491 * optimization finished, #iter = 674 obj = -139.056409, rho = 0.364995 nSV = 552, nBSV = 463 Total nSV = 552 . WARNING: using -h 0 may be faster * optimization finished, #iter = 1018 obj = -361.399366, rho = 0.326164 nSV = 438, nBSV = 369 Total nSV = 438 vectorizing corpus 46%|███████████████▏ | ETA: 0:00:01┌ Warning: using 0 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.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.0 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 0%|▏ | ETA: 0:06:19* optimization finished, #iter = 578 obj = -116.797318, rho = 0.240313 nSV = 471, nBSV = 405 Total nSV = 471 *.* optimization finished, #iter = 0 obj = 0.000000, rho = nan nSV = 0, nBSV = 0 ┌ Warning: using 0 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3, 5], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.0 │ weights: Symbol none │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 0 obj = 0.000000, rho = nan nSV = 0, nBSV = 0 [ Info: TaskFailedException(Task (failed) @0x000071183b0e68b0) *.* optimization finished, #iter = 945 obj = -304.425238, rho = -0.158104 nSV = 477, nBSV = 322 Total nSV = 477 * optimization finished, #iter = 646 obj = -135.491978, rho = -0.335515 nSV = 539, nBSV = 452 Total nSV = 539 .* optimization finished, #iter = 1082 obj = -282.605221, rho = -0.127617 nSV = 495, nBSV = 296 Total nSV = 495 * optimization finished, #iter = 649 obj = -137.644823, rho = -0.363696 nSV = 539, nBSV = 457 Total nSV = 539 [ Info: ignoring configuration due to exception vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03[ Info: TaskFailedException(Task (failed) @0x000071183b0e6a40) vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04[ Info: ignoring configuration due to exception  * optimization finished, #iter = 667 obj = -118.966973, rho = -0.118572 nSV = 483, nBSV = 394 Total nSV = 483 ┌ Warning: using 725 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 957 obj = -307.321164, rho = -0.228345 nSV = 485, nBSV = 323 Total nSV = 485 * optimization finished, #iter = 651 obj = -135.629986, rho = -0.344178 nSV = 535, nBSV = 453 Total nSV = 535 .* optimization finished, #iter = 971 obj = -284.879656, rho = -0.223696 nSV = 492, nBSV = 291 Total nSV = 492 * optimization finished, #iter = 645 obj = -135.691987, rho = -0.382884 nSV = 533, nBSV = 451 Total nSV = 533 .*.* optimization finished, #iter = 1543 obj = -357.544575, rho = -0.417451 nSV = 436, nBSV = 353 Total nSV = 436 .*.* optimization finished, #iter = 2057 obj = -487.251717, rho = 0.149688 nSV = 431, nBSV = 125 Total nSV = 431 .*.* optimization finished, #iter = 2003 obj = -625.484443, rho = 0.224074 nSV = 430, nBSV = 180 Total nSV = 430 * optimization finished, #iter = 599 obj = -115.306302, rho = -0.165163 nSV = 461, nBSV = 393 Total nSV = 461 ┌ 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 10 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .*.* optimization finished, #iter = 1793 obj = -498.560401, rho = -0.052551 nSV = 421, nBSV = 125 Total nSV = 421 .*.* optimization finished, #iter = 1866 obj = -634.623764, rho = -0.011168 nSV = 428, nBSV = 183 Total nSV = 428 .* optimization finished, #iter = 1033 obj = -368.227471, rho = -0.452652 nSV = 452, nBSV = 380 Total nSV = 452 .*.* optimization finished, #iter = 2005 obj = -490.222583, rho = -0.200094 nSV = 447, nBSV = 118 Total nSV = 447 .*.* optimization finished, #iter = 1880 obj = -632.035474, rho = -0.422405 nSV = 433, nBSV = 178 Total nSV = 433 SearchModels iteration 3> population: 8, bsize: 2, queue: 8, observed: 60, best-error: 0.2235043471784991 worst-error: 0.2388407745326634 .*.* optimization finished, #iter = 1952 obj = -635.025674, rho = 0.176364 nSV = 436, nBSV = 185 Total nSV = 436 *.* optimization finished, #iter = 918 obj = -313.679284, rho = 0.213019 nSV = 483, nBSV = 336 Total nSV = 483 *.* optimization finished, #iter = 952 obj = -294.126852, rho = 0.242933 nSV = 468, nBSV = 306 Total nSV = 468 *.* optimization finished, #iter = 881 obj = -310.966047, rho = 0.294792 nSV = 455, nBSV = 329 Total nSV = 455 ┌ Warning: using 725 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 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 900 obj = -248.326460, rho = 0.304708 nSV = 401, nBSV = 260 Total nSV = 401 * optimization finished, #iter = 540 obj = -119.579294, rho = 0.264152 nSV = 467, nBSV = 412 Total nSV = 467 * optimization finished, #iter = 610 obj = -50.289355, rho = 0.272863 nSV = 577, nBSV = 547 Total nSV = 577 .* optimization finished, #iter = 1074 obj = -339.877038, rho = 0.276024 nSV = 439, nBSV = 343 Total nSV = 439 .*.* optimization finished, #iter = 1886 obj = -644.969247, rho = -0.021269 nSV = 430, nBSV = 187 Total nSV = 430 *.* optimization finished, #iter = 851 obj = -312.977978, rho = -0.199300 nSV = 473, nBSV = 332 Total nSV = 473 *.* optimization finished, #iter = 966 obj = -294.595545, rho = -0.209688 nSV = 467, nBSV = 311 Total nSV = 467 *.* optimization finished, #iter = 995 obj = -308.527897, rho = -0.253305 nSV = 462, nBSV = 326 Total nSV = 462 ┌ Warning: using 725 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 1073 obj = -261.732506, rho = 0.020190 nSV = 411, nBSV = 260 Total nSV = 411 * optimization finished, #iter = 635 obj = -121.857055, rho = -0.125569 nSV = 474, nBSV = 406 Total nSV = 474 * optimization finished, #iter = 603 obj = -50.729010, rho = -0.160916 nSV = 572, nBSV = 542 Total nSV = 572 *.* optimization finished, #iter = 1125 obj = -344.871910, rho = -0.272708 nSV = 441, nBSV = 344 Total nSV = 441 .*.* optimization finished, #iter = 1873 obj = -640.207824, rho = -0.428426 nSV = 436, nBSV = 188 Total nSV = 436 *.* optimization finished, #iter = 881 obj = -311.282328, rho = -0.306522 nSV = 475, nBSV = 328 Total nSV = 475 .* optimization finished, #iter = 986 obj = -298.726469, rho = -0.257749 nSV = 476, nBSV = 313 Total nSV = 476 .* optimization finished, #iter = 1031 obj = -313.802015, rho = -0.341149 nSV = 464, nBSV = 333 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.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 = 930 obj = -254.794646, rho = -0.135546 nSV = 394, nBSV = 260 Total nSV = 394 * optimization finished, #iter = 610 obj = -117.265387, rho = -0.140508 nSV = 466, nBSV = 398 Total nSV = 466 * optimization finished, #iter = 599 obj = -49.825945, rho = -0.253395 nSV = 576, nBSV = 535 Total nSV = 576 .* optimization finished, #iter = 1110 obj = -353.122245, rho = -0.459709 nSV = 452, nBSV = 365 Total nSV = 452 SearchModels iteration 4> population: 8, bsize: 2, queue: 10, observed: 70, best-error: 0.2235043471784991 worst-error: 0.2363978963481883 [ Info: TaskFailedException(Task (failed) @0x000071180f339910) *.* optimization finished, #iter = 820 obj = -330.490043, rho = 0.243121 nSV = 462, nBSV = 355 Total nSV = 462 *.* optimization finished, #iter = 923 obj = -313.731896, rho = 0.214745 nSV = 483, nBSV = 334 Total nSV = 483 .* optimization finished, #iter = 1054 obj = -290.687112, rho = 0.267818 nSV = 495, nBSV = 296 Total nSV = 495 *.* optimization finished, #iter = 895 obj = -298.922371, rho = 0.210892 nSV = 488, nBSV = 319 Total nSV = 488 * optimization finished, #iter = 678 obj = -134.722780, rho = 0.389009 nSV = 527, nBSV = 454 Total nSV = 527 [ Info: ignoring configuration due to exception [ Info: TaskFailedException(Task (failed) @0x000071180f339aa0) [ Info: ignoring configuration due to exception *.* optimization finished, #iter = 933 obj = -326.510873, rho = -0.247037 nSV = 467, nBSV = 341 Total nSV = 467 *.* optimization finished, #iter = 820 obj = -313.017929, rho = -0.200249 nSV = 471, nBSV = 332 Total nSV = 471 .* optimization finished, #iter = 988 obj = -292.378870, rho = -0.125073 nSV = 490, nBSV = 307 Total nSV = 490 .* optimization finished, #iter = 964 obj = -298.232926, rho = -0.158444 nSV = 483, nBSV = 316 Total nSV = 483 * optimization finished, #iter = 675 obj = -132.949028, rho = -0.380425 nSV = 521, nBSV = 440 Total nSV = 521 ┌ Warning: using 107 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 34 obj = -21.329980, rho = 0.050339 nSV = 26, nBSV = 18 Total nSV = 26 ┌ Warning: using 78 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], 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 WARNING: training data in only one class. See README for details. Total nSV = 0 ┌ Warning: using 583 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 903 obj = -327.876625, rho = -0.381172 nSV = 460, nBSV = 342 Total nSV = 460 *.* optimization finished, #iter = 886 obj = -311.246548, rho = -0.308348 nSV = 475, nBSV = 328 Total nSV = 475 .* optimization finished, #iter = 990 obj = -291.598049, rho = -0.245575 nSV = 486, nBSV = 299 Total nSV = 486 *.* optimization finished, #iter = 963 obj = -298.198830, rho = -0.229115 nSV = 490, nBSV = 305 Total nSV = 490 * optimization finished, #iter = 644 obj = -134.163105, rho = -0.381467 nSV = 515, nBSV = 445 Total nSV = 515 * optimization finished, #iter = 564 obj = -341.343789, rho = 0.658355 nSV = 384, nBSV = 341 Total nSV = 384 [ Info: precision is zero for label '💔'; #classes=2 ┌ Warning: using 89 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 30 obj = -16.105266, rho = 0.406467 nSV = 21, nBSV = 14 Total nSV = 21 ┌ Warning: using 567 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 499 obj = -344.400628, rho = -0.455665 nSV = 381, nBSV = 339 Total nSV = 381 ┌ Warning: using 59 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], 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 WARNING: training data in only one class. See README for details. Total nSV = 0 ┌ Warning: using 103 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 36 obj = -16.809119, rho = -0.357310 nSV = 22, nBSV = 14 Total nSV = 22 [ Info: precision is zero for label '💔'; #classes=2 ┌ Warning: using 569 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 457 obj = -315.135052, rho = -0.455892 nSV = 350, nBSV = 310 Total nSV = 350 ┌ Warning: using 64 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], 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 WARNING: training data in only one class. See README for details. Total nSV = 0 [ Info: precision is zero for label '💔'; #classes=2 SearchModels> stop by convergence error={MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5, 7], Int8[4], 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 0.9099999999999999 LIBSVMConfig } => 0.2363978963481883, iter=5 (of 16) .* optimization finished, #iter = 1544 obj = -425.552296, rho = -0.242396 nSV = 701, nBSV = 437 Total nSV = 701 ┌ Info: ("-- microtc_kfolds - perf best_lists[1]:", 1, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], 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 7 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.2235043471784991) ┌ Info: ("-- microtc_kfolds - perf best_lists[2]:", 2, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(3, 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 7 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.22535498378587937) ┌ Info: ("-- microtc_kfolds - perf best_lists[3]:", 3, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 2.6 │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.84 │ weights: Symbol none └ LIBSVMConfig }, 0.2259797576732202) ┌ Info: ("-- microtc_kfolds - perf best_lists[4]:", 4, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.84 │ weights: Symbol none └ LIBSVMConfig }, 0.22671126454783364) ┌ Info: ("-- microtc_kfolds - perf best_lists[5]:", 5, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[5], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 2.6 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.84 │ weights: Symbol none └ LIBSVMConfig }, 0.22758386489687366) ┌ Info: ("-- microtc_kfolds - perf best_lists[6]:", 6, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.FreqWeighting} │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.7 │ weights: Symbol none └ LIBSVMConfig }, 0.23426634184201356) ┌ Info: ("-- microtc_kfolds - perf best_lists[7]:", 7, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.BinaryLocalWeighting} │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ smooth: Float64 1.5384615384615383 │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.7 │ weights: Symbol none └ LIBSVMConfig }, 0.23604816725929112) ┌ Info: ("-- microtc_kfolds - perf best_lists[8]:", 8, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5, 7], Int8[4], 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 0.9099999999999999 └ LIBSVMConfig }, 0.2363978963481883) ┌ Info: *** Performance microtc_kfolds on test: └ sc = (microf1 = 0.7846481876332623, precision = 0.7846481876332623, macroprecision = 0.785744246270562, recall = 0.7846481876332623, macrorecall = 0.7849647170085843, macrof1 = 0.7845502385598173, accuracy = 0.7846481876332623, classf1 = Dict{Any, Any}("💔" => 0.789144050104384, "♡" => 0.7799564270152506), classprecision = Dict{Any, Any}("💔" => 0.7651821862348178, "♡" => 0.8063063063063063), classrecall = Dict{Any, Any}("💔" => 0.8146551724137931, "♡" => 0.7552742616033755)) Test Summary: | Pass Total Time microtc | 2 2 3m17.4s Testing TextClassification tests passed Testing completed after 211.59s PkgEval succeeded after 293.8s