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Broken examples of MARS #3

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JinraeKim opened this issue May 8, 2021 · 0 comments
Open

Broken examples of MARS #3

JinraeKim opened this issue May 8, 2021 · 0 comments

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@JinraeKim
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MARS example seems broken.

julia> using MultivariateFunctions


julia> using Random


julia> using DataFrames


julia> using Distributions


julia> using DataStructures


julia> Random.seed!(1992)

MersenneTwister(1992)

julia> nObs = 1000

1000

julia> dd = DataFrame()

0×0 DataFrame


julia> dd[:x] = rand( Normal(),nObs) + 0.1 .* rand( Normal(),nObs)

1000-element Vector{Float64}:
 -1.5194924275498334
  0.5844430822023597
 -0.5497375840875416
  1.2817822410251947
 -0.19060992619057496
 -0.47048055970036007
  0.6818583626457018
 -0.8554162326299839
 -1.3695617445991064
  1.0953038238849109
 -1.699099302747622
 -0.6780117585650899
  1.4192925709333952
  0.42807807496395617
  1.0352222109740405
  ⋮
 -0.10688015795519358
  0.9430607035612268
 -0.20183783555815388
  0.6460559374899443
  0.026095413811565132
 -0.6833593785111719
  0.07431322076156123
  0.2014010440942761
 -0.7793609005754738
  0.39473020003581993
 -0.5449445196399865
 -1.0193446965787274
 -1.2568550834023509
 -1.178925006150981
 -1.7098961465470663

julia> dd[:z] = rand( Normal(),nObs) + 0.1 .* rand( Normal(),nObs)

1000-element Vector{Float64}:
 -1.1750705521302567
  0.9069556021870959
  0.20960467727966883
 -0.016935093159266515
  0.32734357936941416
 -0.4583427779582831
 -1.2525094667318868
 -0.8855902909295208
 -0.7514670213109499
  0.37216278394183117
 -0.28668599160737784
  0.685074075486414
  0.3184272652769276
 -0.5276955662875091
  0.38991220787275427
  ⋮
  1.3861777686416614
  1.0765345340164898
 -0.1652608047507846
  0.719043828393897
  0.8701332884169406
  0.767772517601333
  1.0669198445676553
  0.11335691897963737
  1.5910107187781224
 -0.7128766324283243
 -0.7931006582401271
  0.4976775640182734
 -0.6536206642622754
 -0.31287850605786915
 -0.7850092399544946

julia> dd[:w] = (0.5 .* rand( Normal(),nObs)) .+ 0.7.*(dd[:z] .- dd[:x]) + 0.1 .* rand( Normal(),nObs)

1000-element Vector{Float64}:
 -0.0019189980122132633
  0.4725494429492194
 -0.0675946792898773
 -1.157130519654445
  0.14522767354377503
 -0.005382762866542205
 -1.2005096892042109
  0.13085627620806112
  0.05612174029581857
 -0.6172526046137324
  1.4778484610424196
  1.054709698408579
 -1.2271058467494558
 -0.5353888688122925
 -0.7294356191124297
  ⋮
  0.615069475148343
  0.2181079071913068
  0.09486662765477802
  0.4697963855574916
  0.15935631987862492
  0.5755374838341898
  0.7882977393176024
 -0.2396016872530803
  1.5271293107037496
 -0.8947695470014021
 -1.072537042317404
  0.04893462310292454
  0.026454764346978646
  0.5885500404818271
  1.3937893199788063

julia> dd[:y] = (dd[:x] .*dd[:w] ) .* (dd[:z] .- dd[:w]) .+ dd[:x] + rand( Normal(),nObs)

1000-element Vector{Float64}:
 -2.3487840899451675
  1.0087441497559786
 -2.280470624644659
 -0.9242658326625207
  0.815181257773693
 -0.5031071711999816
  0.30192536890203986
 -2.035262792736697
  0.03194223795679596
  1.13595311468184
  3.7442843912932613
  0.6009073972935214
 -0.44822283045481404
  0.6405293235510074
 -0.16269142011610555
  ⋮
 -2.4459349487643323
  1.7869773765861698
 -2.046579053983459
  0.9006917341756171
  0.41441684404908374
 -0.010334525049836674
  1.0941056908369529
  1.4286676478281712
 -0.26487929690458845
  0.5752509993328735
 -0.5676424153457388
 -1.9430060207426862
 -2.6246440845770023
 -1.5385613151541713
  4.127923473876518

julia> dd[7,:y] = 1.0

1.0

julia> y = :y

:y

julia> x_variables = Set{Symbol}([:w, :x, :z])
Set{Symbol} with 3 elements:
  :w
  :z
  :x

julia> number_of_divisions = 7
7

julia> rp_4, rp_reg_4 = create_recursive_partitioning(dd, y, x_variables, number_of_divisions; rel_tol = 1e-3)

ERROR: MethodError: Cannot `convert` an object of type String to an object of type Symbol
Closest candidates are:
  convert(::Type{S}, ::T) where {S, T<:CategoricalValue} at /Users/jinrae/.julia/packages/CategoricalArrays/0ZAbp/src/value.jl:68
  convert(::Type{T}, ::T) where T at essentials.jl:205
  Symbol(::String) at boot.jl:478
  ...
Stacktrace:
  [1] setindex!(h::Dict{Symbol, Float64}, v0::Float64, key0::String)
    @ Base ./dict.jl:374
  [2] evaluate(f::Piecewise_Function, coordinates::DataFrame)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/0_structs_and_generic_reversals.jl:717
  [3] _broadcast_getindex_evalf
    @ ./broadcast.jl:648 [inlined]
  [4] _broadcast_getindex
    @ ./broadcast.jl:621 [inlined]
  [5] getindex
    @ ./broadcast.jl:575 [inlined]
  [6] macro expansion
    @ ./broadcast.jl:984 [inlined]
  [7] macro expansion
    @ ./simdloop.jl:77 [inlined]
  [8] copyto!(dest::Vector{Vector{Union{Float64, MultivariateFunction}}}, bc::Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(evaluate), Tuple{Vector{Piecewise_Function}, Base.RefValue{DataFrame}}})
    @ Base.Broadcast ./broadcast.jl:983
  [9] copyto!
    @ ./broadcast.jl:936 [inlined]
 [10] copy
    @ ./broadcast.jl:908 [inlined]
 [11] materialize
    @ ./broadcast.jl:883 [inlined]
 [12] create_ols_approximation(dd::DataFrame, y::Symbol, model::Vector{Piecewise_Function}; allowrankdeficient::Bool)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/5_ols_regression.jl:43
 [13] create_ols_approximation
    @ ~/.julia/packages/MultivariateFunctions/a2IXB/src/5_ols_regression.jl:43 [inlined]
 [14] optimise_given_specific_split(dd::DataFrame, y::Symbol, array_of_funcs::Vector{Piecewise_Function}, ind::Int64, split_variable::Symbol, split_point::Float64, SplitFunction::typeof(MultivariateFunctions.add_split_with_step_function), removeSplitFunction::Bool)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:28
 [15] #56
    @ ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:36 [inlined]
 [16] optimize(f::MultivariateFunctions.var"#56#57"{DataFrame, Symbol, Vector{Piecewise_Function}, Int64, Symbol, typeof(MultivariateFunctions.add_split_with_step_function), Bool}, x_lower::Float64, x_upper::Float64, mo::Optim.Brent; rel_tol::Float64, abs_tol::Float64, iterations::Int64, store_trace::Bool, show_trace::Bool, callback::Nothing, show_every::Int64, extended_trace::Bool)
    @ Optim ~/.julia/packages/Optim/TNmSw/src/univariate/solvers/brent.jl:69
 [17] #optimize#80
    @ ~/.julia/packages/Optim/TNmSw/src/univariate/optimize/interface.jl:21 [inlined]
 [18] optimise_split(dd::DataFrame, y::Symbol, array_of_funcs::Vector{Piecewise_Function}, ind::Int64, split_variable::Symbol, rel_tol::Float64, SplitFunction::Function, removeSplitFunction::Bool)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:36
 [19] create_recursive_partitioning(dd::DataFrame, y::Symbol, x_variables::Set{Symbol}, MaxM::Int64; rel_tol::Float64)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:65
 [20] top-level scope
    @ REPL[17]:1

julia>

julia> rp_1, rp_reg_1 = create_mars_spline(dd, y, x_variables, number_of_divisions; rel_tol = 1e-3)
ERROR: MethodError: Cannot `convert` an object of type String to an object of type Symbol
Closest candidates are:
  convert(::Type{S}, ::T) where {S, T<:CategoricalValue} at /Users/jinrae/.julia/packages/CategoricalArrays/0ZAbp/src/value.jl:68
  convert(::Type{T}, ::T) where T at essentials.jl:205
  Symbol(::String) at boot.jl:478
  ...
Stacktrace:
  [1] setindex!(h::Dict{Symbol, Float64}, v0::Float64, key0::String)
    @ Base ./dict.jl:374
  [2] evaluate(f::Piecewise_Function, coordinates::DataFrame)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/0_structs_and_generic_reversals.jl:717
  [3] _broadcast_getindex_evalf
    @ ./broadcast.jl:648 [inlined]
  [4] _broadcast_getindex
    @ ./broadcast.jl:621 [inlined]
  [5] getindex
    @ ./broadcast.jl:575 [inlined]
  [6] macro expansion
    @ ./broadcast.jl:984 [inlined]
  [7] macro expansion
    @ ./simdloop.jl:77 [inlined]
  [8] copyto!(dest::Vector{Vector{Union{Float64, MultivariateFunction}}}, bc::Base.Broadcast.Broadcasted{Nothing, Tuple{Base.OneTo{Int64}}, typeof(evaluate), Tuple{Vector{Piecewise_Function}, Base.RefValue{DataFrame}}})
    @ Base.Broadcast ./broadcast.jl:983
  [9] copyto!
    @ ./broadcast.jl:936 [inlined]
 [10] copy
    @ ./broadcast.jl:908 [inlined]
 [11] materialize
    @ ./broadcast.jl:883 [inlined]
 [12] create_ols_approximation(dd::DataFrame, y::Symbol, model::Vector{Piecewise_Function}; allowrankdeficient::Bool)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/5_ols_regression.jl:43
 [13] create_ols_approximation
    @ ~/.julia/packages/MultivariateFunctions/a2IXB/src/5_ols_regression.jl:43 [inlined]
 [14] optimise_given_specific_split(dd::DataFrame, y::Symbol, array_of_funcs::Vector{Piecewise_Function}, ind::Int64, split_variable::Symbol, split_point::Float64, SplitFunction::typeof(MultivariateFunctions.add_split_with_max_function), removeSplitFunction::Bool)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:28
 [15] #56
    @ ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:36 [inlined]
 [16] optimize(f::MultivariateFunctions.var"#56#57"{DataFrame, Symbol, Vector{Piecewise_Function}, Int64, Symbol, typeof(MultivariateFunctions.add_split_with_max_function), Bool}, x_lower::Float64, x_upper::Float64, mo::Optim.Brent; rel_tol::Float64, abs_tol::Float64, iterations::Int64, store_trace::Bool, show_trace::Bool, callback::Nothing, show_every::Int64, extended_trace::Bool)
    @ Optim ~/.julia/packages/Optim/TNmSw/src/univariate/solvers/brent.jl:69
 [17] #optimize#80
    @ ~/.julia/packages/Optim/TNmSw/src/univariate/optimize/interface.jl:21 [inlined]
 [18] optimise_split(dd::DataFrame, y::Symbol, array_of_funcs::Vector{Piecewise_Function}, ind::Int64, split_variable::Symbol, rel_tol::Float64, SplitFunction::Function, removeSplitFunction::Bool)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:36
 [19] create_mars_spline(dd::DataFrame, y::Symbol, x_variables::Set{Symbol}, MaxM::Int64; rel_tol::Float64)
    @ MultivariateFunctions ~/.julia/packages/MultivariateFunctions/a2IXB/src/6_HighDimensionalApproximation.jl:104
 [20] top-level scope
    @ REPL[18]:1

Notes

  • versioninfo()
Julia Version 1.6.1
Commit 6aaedecc44 (2021-04-23 05:59 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin18.7.0)
  CPU: Apple M1
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-11.0.1 (ORCJIT, westmere)
  • MultivariateFunctions v0.1.7
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@JinraeKim