learning_curve throwing nested task error #155
Description
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] RandomJulia 1.6.3