Open ablaom opened 2 years ago
X, y = make_blobs() model = (@load RandomForestClassifier pkg=DecisionTree)() mach = machine(model, X, y) r = range(model, :n_trees, lower=10, upper=70, scale=:log10) many_curves = learning_curve(mach, range=r, resampling=Holdout(), measure=cross_entropy, rng_name=:rng, rngs=1) Evaluating Learning curve with 1 rngs: 0%[> ] ETA: N/A┌ Error: Problem fi tting the machine Machine{RandomForestClassifier,…}. └ @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:533 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine Machine{Resampler{Holdout},…}. └ @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:533 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine Machine{ProbabilisticTunedModel{Grid,…},…}. └ @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:533 [ Info: Running type checks... [ Info: Type checks okay. ERROR: TaskFailedException Stacktrace: [1] wait @ ./task.jl:322 [inlined] [2] threading_run(func::Function) @ Base.Threads ./threadingconstructs.jl:34 [3] macro expansion @ ./threadingconstructs.jl:93 [inlined] [4] build_forest(labels::Vector{UInt32}, features::Matrix{Float64}, n_subfeatures::Int64, n_trees::Int64, partial_sampling::Float64, max_depth::Int64, min_samples_leaf::Int64, min_samples_split::Int64, min_purity_increase::Float64; rng::Random.MersenneTwister) @ DecisionTree ~/.julia/packages/DecisionTree/iWCbW/src/classification/main.jl:223 [5] fit(m::MLJDecisionTreeInterface.RandomForestClassifier, verbosity::Int64, X::DataFrames.DataFrame, y::CategoricalVector{Int64, UInt32, Int64, CategoricalValue{Int64, UInt32}, Union{}}) @ MLJDecisionTreeInterface ~/.julia/packages/MLJDecisionTreeInterface/RZmUr/src/MLJDecisionTreeInterface.jl:200 [6] fit_only!(mach::Machine{MLJDecisionTreeInterface.RandomForestClassifier, true}; rows:: Vector{Int64}, verbosity::Int64, force::Bool) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:531 [7] #fit!#103 @ ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:598 [inlined] [8] fit_and_extract_on_fold @ ~/.julia/packages/MLJBase/HZmTU/src/resampling.jl:1088 [inlined] [9] (::MLJBase.var"#276#277"{MLJBase.var"#fit_and_extract_on_fold#299"{Vector{Tuple{Vector{Int64}, Vector{Int64}}}, Nothing, Nothing, Int64, Vector{LogLoss{Float64}}, Vector{typeof(predict)}, Bool, Bool, CategoricalVector{Int64, UInt32, Int64, CategoricalValue{Int64, UInt32}, Union{}}, DataFrames.DataFrame}, Machine{MLJDecisionTreeInterface.RandomForestClassifier, true}, Int64, ProgressMeter.Progress})(k::Int64) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/resampling.jl:932 [10] mapreduce_first @ ./reduce.jl:392 [inlined] [11] _mapreduce(f::MLJBase.var"#276#277"{MLJBase.var"#fit_and_extract_on_fold#299"{Vector{Tuple{Vector{Int64}, Vector{Int64}}}, Nothing, Nothing, Int64, Vector{LogLoss{Float64}}, Vector{typeof(predict)}, Bool, Bool, CategoricalVector{Int64, UInt32, Int64, CategoricalValue{Int64, UInt32}, Union{}}, DataFrames.DataFrame}, Machine{MLJDecisionTreeInterface.RandomForestClassifier, true}, Int64, ProgressMeter.Progress}, op::typeof(vcat), #unused#::IndexLinear, A::UnitRange{Int64}) @ Base ./reduce.jl:403 [12] _mapreduce_dim @ ./reducedim.jl:318 [inlined] [13] #mapreduce#672 @ ./reducedim.jl:310 [inlined] [14] mapreduce @ ./reducedim.jl:310 [inlined] [15] _evaluate!(func::MLJBase.var"#fit_and_extract_on_fold#299"{Vector{Tuple{Vector{Int64}, Vector{Int64}}}, Nothing, Nothing, Int64, Vector{LogLoss{Float64}}, Vector{typeof(predict)}, Bool, Bool, CategoricalVector{Int64, UInt32, Int64, CategoricalValue{Int64, UInt32}, Union{}}, DataFrames.DataFrame}, mach::Machine{MLJDecisionTreeInterface.RandomForestClassifier, true}, #unused#::CPU1{Nothing}, nfolds::Int64, verbosity::Int64) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/resampling.jl:931 [16] evaluate!(mach::Machine{MLJDecisionTreeInterface.RandomForestClassifier, true}, resampling::Vector{Tuple{Vector{Int64}, Vector{Int64}}}, weights::Nothing, class_weights::Nothing, rows::Nothing, verbosity::Int64, repeats::Int64, measures::Vector{LogLoss{Float64}}, operations::Vector{typeof(predict)}, acceleration::CPU1{Nothing}, force::Bool) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/resampling.jl:1126 [17] evaluate!(::Machine{MLJDecisionTreeInterface.RandomForestClassifier, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{LogLoss{Float64}}, ::Vector{typeof(predict)}, ::CPU1{Nothing}, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/resampling.jl:1193 [18] fit(::Resampler{Holdout}, ::Int64, ::DataFrames.DataFrame, ::CategoricalVector{Int64, UInt32, Int64, CategoricalValue{Int64, UInt32}, Union{}}) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/resampling.jl:1337 [19] fit_only!(mach::Machine{Resampler{Holdout}, false}; rows::Nothing, verbosity::Int64, force::Bool) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:531 [20] #fit!#103 @ ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:598 [inlined] [21] event!(metamodel::MLJDecisionTreeInterface.RandomForestClassifier, resampling_machine::Machine{Resampler{Holdout}, false}, verbosity::Int64, tuning::Grid, history::Nothing, state ::NamedTuple{(:models, :fields, :parameter_scales, :models_delivered), Tuple{Vector{MLJDecisionTreeInterface.RandomForestClassifier}, Vector{Symbol}, Vector{Symbol}, Bool}}) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/tuned_models.jl:395 [22] #35 @ ~/.julia/packages/MLJTuning/efiDR/src/tuned_models.jl:433 [inlined] [23] iterate @ ./generator.jl:47 [inlined] [24] _collect(c::Vector{MLJDecisionTreeInterface.RandomForestClassifier}, itr::Base.Generator{Vector{MLJDecisionTreeInterface.RandomForestClassifier}, MLJTuning.var"#35#36"{Machine{Resampler{Holdout}, false}, Int64, Grid, Nothing, NamedTuple{(:models, :fields, :parameter_scales, :models_delivered), Tuple{Vector{MLJDecisionTreeInterface.RandomForestClassifier}, Vector{Symbol}, Vector{Symbol}, Bool}}, ProgressMeter.Progress}}, #unused#::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:695 [25] collect_similar @ ./array.jl:606 [inlined] [26] map @ ./abstractarray.jl:2294 [inlined] [27] assemble_events!(metamodels::Vector{MLJDecisionTreeInterface.RandomForestClassifier}, resampling_machine::Machine{Resampler{Holdout}, false}, verbosity::Int64, tuning::Grid, history::Nothing, state::NamedTuple{(:models, :fields, :parameter_scales, :models_delivered), Tuple{Vector{MLJDecisionTreeInterface.RandomForestClassifier}, Vector{Symbol}, Vector{Symbol}, Bool}}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/tuned_models.jl:432 [28] build!(history::Nothing, n::Int64, tuning::Grid, model::MLJDecisionTreeInterface.RandomForestClassifier, model_buffer::Channel{Any}, state::NamedTuple{(:models, :fields, :parameter_scales, :models_delivered), Tuple{Vector{MLJDecisionTreeInterface.RandomForestClassifier}, Vector{Symbol}, Vector{Symbol}, Bool}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/tuned_models.jl:625 [29] fit(::MLJTuning.ProbabilisticTunedModel{Grid, MLJDecisionTreeInterface.RandomForestClassifier}, ::Int64, ::DataFrames.DataFrame, ::CategoricalVector{Int64, UInt32, Int64, CategoricalValue{Int64, UInt32}, Union{}}) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/tuned_models.jl:704 [30] fit_only!(mach::Machine{MLJTuning.ProbabilisticTunedModel{Grid, MLJDecisionTreeInterface.RandomForestClassifier}, true}; rows::Nothing, verbosity::Int64, force::Bool) @ MLJBase ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:531 [31] #fit!#103 @ ~/.julia/packages/MLJBase/HZmTU/src/machines.jl:598 [inlined] [32] (::MLJTuning.var"#61#62"{Machine{MLJTuning.ProbabilisticTunedModel{Grid, MLJDecisionTreeInterface.RandomForestClassifier}, true}, Nothing, Symbol, Int64, ProgressMeter.Progress}) (rng::Random.MersenneTwister) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/learning_curves.jl:231 [33] mapreduce_first @ ./reduce.jl:392 [inlined] [34] _mapreduce(f::MLJTuning.var"#61#62"{Machine{MLJTuning.ProbabilisticTunedModel{Grid, MLJDecisionTreeInterface.RandomForestClassifier}, true}, Nothing, Symbol, Int64, ProgressMeter.Progress}, op::typeof(MLJTuning._collate), #unused#::IndexLinear, A::Vector{Random.MersenneTwister}) @ Base ./reduce.jl:403 [35] _mapreduce_dim @ ./reducedim.jl:318 [inlined] [36] #mapreduce#672 @ ./reducedim.jl:310 [inlined] [37] mapreduce @ ./reducedim.jl:310 [inlined] [38] _tuning_results(rngs::Vector{Random.MersenneTwister}, acceleration::CPU1{Nothing}, tuned::Machine{MLJTuning.ProbabilisticTunedModel{Grid, MLJDecisionTreeInterface.RandomForestClassifier}, true}, rows::Nothing, rng_name::Symbol, verbosity::Int64) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/learning_curves.jl:229 [39] learning_curve(::MLJDecisionTreeInterface.RandomForestClassifier, ::MLJBase.Source, :: Vararg{MLJBase.Source, N} where N; resolution::Int64, resampling::Holdout, weights::Nothing, measures::Nothing, measure::LogLoss{Float64}, rows::Nothing, operation::Nothing, ranges::Nothing, range::MLJBase.NumericRange{Int64, MLJBase.Bounded, Symbol}, repeats::Int64, acceleration::CPU1{Nothing}, acceleration_grid::CPU1{Nothing}, verbosity::Int64, rngs::Int64, rng_name::Symbol, check_measure::Bool) @ MLJTuning ~/.julia/packages/MLJTuning/efiDR/src/learning_curves.jl:173 [40] #learning_curve#58 @ ~/.julia/packages/MLJTuning/efiDR/src/learning_curves.jl:92 [inlined] [41] top-level scope @ REPL[44]:1 nested task error: AssertionError: length(ints) == 501 Stacktrace: [1] mt_setfull!(r::Random.MersenneTwister, #unused#::Type{UInt64}) @ Random /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/RNGs.jl:260 [2] reserve1 @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/RNGs.jl:291 [inlined] [3] mt_pop! @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/RNGs.jl:296 [inlined] [4] rand @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/RNGs.jl:464 [inlined] [5] rand @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/Random.jl:256 [inlined] [6] rand(rng::Random.MersenneTwister, sp::Random.SamplerRangeNDL{UInt64, Int64}) @ Random /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/generation.jl:332 [7] rand! @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/Random.jl:271 [inlined] [8] rand!(rng::Random.MersenneTwister, A::Vector{Int64}, X::UnitRange{Int64}) @ Random /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/Random.jl:266 [9] rand @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/Random.jl:279 [inlined] [10] rand @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Random/src/Random.jl:282 [inlined] [11] macro expansion @ ~/.julia/packages/DecisionTree/iWCbW/src/classification/main.jl:224 [inlined] [12] (::DecisionTree.var"#62#threadsfor_fun#22"{Random.MersenneTwister, Vector{UInt32}, Matrix{Float64}, Int64, Int64, Int64, Float64, DecisionTree.var"#20#21"{Vector{Float64}}, Vector{Union{DecisionTree.Leaf{UInt32}, DecisionTree.Node{Float64, UInt32}}}, Int64, Int64, UnitRange{Int64}})(onethread::Bool) @ DecisionTree ./threadingconstructs.jl:81 [13] (::DecisionTree.var"#62#threadsfor_fun#22"{Random.MersenneTwister, Vector{UInt32}, Matrix{Float64}, Int64, Int64, Int64, Float64, DecisionTree.var"#20#21"{Vector{Float64}}, Vector{Union{DecisionTree.Leaf{UInt32}, DecisionTree.Node{Float64, UInt32}}}, Int64, Int64, UnitRange{Int64}})() @ DecisionTree ./threadingconstructs.jl:48
(MachineLearningInJulia2020) pkg> status Status `~/Google Drive/Julia/MLJ/MachineLearningInJulia2020/Project.toml` [336ed68f] CSV v0.9.6 [324d7699] CategoricalArrays v0.10.1 [ed09eef8] ComputationalResources v0.3.2 [a93c6f00] DataFrames v1.2.2 [7806a523] DecisionTree v0.10.11 [31c24e10] Distributions v0.25.18 [f6006082] EvoTrees v0.8.4 [98b081ad] Literate v2.9.3 [add582a8] MLJ v0.16.9 [a7f614a8] MLJBase v0.18.23 [d354fa79] MLJClusteringInterface v0.1.4 [094fc8d1] MLJFlux v0.2.5 [6ee0df7b] MLJLinearModels v0.5.6 [d491faf4] MLJModels v0.14.12 [1b6a4a23] MLJMultivariateStatsInterface v0.2.2 [5ae90465] MLJScikitLearnInterface v0.1.10 [b8a86587] NearestNeighbors v0.4.9 [a03496cd] PlotlyBase v0.8.18 [91a5bcdd] Plots v1.22.4 [321657f4] ScientificTypes v2.3.0 [2913bbd2] StatsBase v0.33.10 [bd369af6] Tables v1.6.0 [b8865327] UnicodePlots v2.4.6 [9a3f8284] Random
Julia 1.6.3
Julia 1.6.3