search

JuliaDynamics / ComplexityMeasures.jl

Estimators for probabilities, entropies, and other complexity measures derived from data in the context of nonlinear dynamics and complex systems
MIT License
48 stars 11 forks source link

`eachindex` for `Probabilities` is ambiguous #369

Closed kahaaga closed 5 months ago

kahaaga commented 5 months ago

While working for a fix for #368, I encountered this (on ComplexityMeasures v3.0.2, Julia 1.9.4):

julia> using ComplexityMeasures

julia> s = StatisticalComplexity(def = ShannonExtropy(), o = Dispersion(), pest = Shrinkage())
StatisticalComplexity{Distances.JSDivergence, Shrinkage{Nothing, Nothing}, Dispersion{GaussianCDFEncoding}, ShannonExtropy{Int64}}(Distances.JSDivergence(), ShannonExtropy{Int64}(2), Shrinkage{Nothing, Nothing}(nothing, nothing), Dispersion{GaussianCDFEncoding}(GaussianCDFEncoding, 3, 2, 1, false), Base.RefValue{Float64}(0.0))

julia> entropy_complexity_curves(s)
ERROR: MethodError: eachindex(::Probabilities{Float64, 1, Symbol}) is ambiguous.

Candidates:
  eachindex(A::AbstractArray, B::AbstractArray...)
    @ Base abstractarray.jl:377
  eachindex(d::Probabilities{T, N}, args...) where {T, N}
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/probabilities.jl:97
  eachindex(A::AbstractVector)
    @ Base abstractarray.jl:314
  eachindex(A::AbstractArray)
    @ Base abstractarray.jl:371

Possible fix, define
  eachindex(::Probabilities{T, 1}) where T

Stacktrace:
  [1] first(a::Probabilities{Float64, 1, Symbol})
    @ Base ./abstractarray.jl:445
  [2] _mean(f::typeof(identity), A::Probabilities{Float64, 1, Symbol}, dims::Colon)
    @ Statistics /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:176
  [3] #mean#2
    @ /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:164 [inlined]
  [4] mean
    @ /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:164 [inlined]
  [5] _var(A::Probabilities{Float64, 1, Symbol}, corrected::Bool, mean::Nothing, #unused#::Colon)
    @ Statistics /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:379
  [6] #var#15
    @ /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:368 [inlined]
  [7] var
    @ /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:368 [inlined]
  [8] _std(A::Probabilities{Float64, 1, Symbol}, corrected::Bool, mean::Nothing, #unused#::Colon)
    @ Statistics /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:460
  [9] #std#17
    @ /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:449 [inlined]
 [10] std
    @ /Applications/Julia-1.9.app/Contents/Resources/julia/share/julia/stdlib/v1.9/Statistics/src/Statistics.jl:449 [inlined]
 [11] codify(est::Dispersion{GaussianCDFEncoding}, x::Probabilities{Float64, 1, Symbol})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/outcome_spaces/dispersion.jl:101
 [12] counts_and_outcomes(o::Dispersion{GaussianCDFEncoding}, x::Probabilities{Float64, 1, Symbol})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/outcome_spaces/dispersion.jl:75
 [13] probabilities_and_outcomes(est::RelativeAmount, outcomemodel::Dispersion{GaussianCDFEncoding}, x::Probabilities{Float64, 1, Symbol})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/probabilities_estimators/RelativeAmount.jl:53
 [14] probabilities_and_outcomes(est::Shrinkage{Nothing, Nothing}, outcomemodel::Dispersion{GaussianCDFEncoding}, x::Probabilities{Float64, 1, Symbol})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/probabilities_estimators/Shrinkage.jl:65
 [15] probabilities
    @ ~/Documents/Repos/ComplexityMeasures.jl/src/core/probabilities.jl:272 [inlined]
 [16] information(hest::PlugIn{ShannonExtropy{Int64}}, pest::Shrinkage{Nothing, Nothing}, o::Dispersion{GaussianCDFEncoding}, x::Probabilities{Float64, 1, Symbol})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/discrete_info_estimators/plugin.jl:38
 [17] information
    @ ~/Documents/Repos/ComplexityMeasures.jl/src/core/information_functions.jl:99 [inlined]
 [18] complexity(c::StatisticalComplexity{Distances.JSDivergence, Shrinkage{Nothing, Nothing}, Dispersion{GaussianCDFEncoding}, ShannonExtropy{Int64}}, p::Probabilities{Float64, 1, Symbol})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/complexity_measures/statistical_complexity.jl:114
 [19] entropy_complexity_curves(c::StatisticalComplexity{Distances.JSDivergence, Shrinkage{Nothing, Nothing}, Dispersion{GaussianCDFEncoding}, ShannonExtropy{Int64}}; num_max::Int64, num_min::Int64)
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/complexity_measures/statistical_complexity.jl:173
 [20] entropy_complexity_curves(c::StatisticalComplexity{Distances.JSDivergence, Shrinkage{Nothing, Nothing}, Dispersion{GaussianCDFEncoding}, ShannonExtropy{Int64}})
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/complexity_measures/statistical_complexity.jl:157
 [21] top-level scope
    @ REPL[22]:1

It turns out this has nothing to do with the statistical complexity: 

julia> p = probabilities(RelativeAmount(), Dispersion(), rand(50))
 Probabilities{Float64,1} over 9 outcomes
 [1, 1]  0.14285714285714285
 [1, 2]  0.08163265306122448
 [1, 3]  0.14285714285714285
 [2, 1]  0.14285714285714285
 [2, 2]  0.061224489795918366
 [2, 3]  0.061224489795918366
 [3, 1]  0.10204081632653061
 [3, 2]  0.10204081632653061
 [3, 3]  0.16326530612244897

julia> eachindex(p)
ERROR: MethodError: eachindex(::Probabilities{Float64, 1, Symbol}) is ambiguous.

Candidates:
  eachindex(A::AbstractArray, B::AbstractArray...)
    @ Base abstractarray.jl:377
  eachindex(d::Probabilities{T, N}, args...) where {T, N}
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/probabilities.jl:97
  eachindex(A::AbstractVector)
    @ Base abstractarray.jl:314
  eachindex(A::AbstractArray)
    @ Base abstractarray.jl:371

Possible fix, define
  eachindex(::Probabilities{T, 1}) where T

Stacktrace:
 [1] top-level scope
   @ REPL[19]:1
kahaaga commented 5 months ago

This is also the case for Counts:

julia> c = counts(Dispersion(), rand(50))
 Counts{Int64,1} over 9 outcomes
 [1, 1]  5
 [1, 2]  2
 [1, 3]  7
 [2, 1]  2
 [2, 2]  8
 [2, 3]  6
 [3, 1]  7
 [3, 2]  6
 [3, 3]  6

julia> eachindex(c)
ERROR: MethodError: eachindex(::Counts{Int64, 1, Symbol}) is ambiguous.

Candidates:
  eachindex(A::AbstractArray, B::AbstractArray...)
    @ Base abstractarray.jl:377
  eachindex(c::Counts, args...)
    @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/counts.jl:92
  eachindex(A::AbstractVector)
    @ Base abstractarray.jl:314
  eachindex(A::AbstractArray)
    @ Base abstractarray.jl:371

Possible fix, define
  eachindex(::Counts{T, 1} where T<:Integer)

Stacktrace:
 [1] top-level scope
   @ REPL[8]:1
kahaaga commented 5 months ago

Some hints: 

julia> using ComplexityMeasures

julia> using Test; detect_ambiguities(ComplexityMeasures)
9-element Vector{Tuple{Method, Method}}:
 (eachindex(d::Probabilities{T, N, S}, args...) where {T, N, S} @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/probabilities.jl:97, eachindex(A::AbstractArray, B::AbstractArray) @ Base abstractarray.jl:373)
 (probabilities(x, est::OutcomeSpace) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/deprecations.jl:49, probabilities(est::ValueBinning, x) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/outcome_spaces/value_binning.jl:66)
 (eachindex(c::Counts{T, N}, args...) where {T<:Integer, N} @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/counts.jl:92, eachindex(A::AbstractArray, B::AbstractArray) @ Base abstractarray.jl:373)
 (information(est::DifferentialInfoEstimator, args...) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/information_functions.jl:267, information(est::InformationMeasureEstimator, probest::ProbabilitiesEstimator, args...) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/information_measures.jl:91)
 ((var"#ctor-self#"::Type{Outcome{T}} where T<:Integer)(num) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/outcome.jl:14, (::Type{T})(x::Base.TwicePrecision) where T<:Number @ Base twiceprecision.jl:266)
 (probabilities(o::OutcomeSpace, x) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/probabilities.jl:261, probabilities(x, est::OutcomeSpace) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/deprecations.jl:49)
 (eachindex(c::Counts{T, N}, args...) where {T<:Integer, N} @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/counts.jl:92, eachindex(A::AbstractArray, B::AbstractArray...) @ Base abstractarray.jl:377)
 (eachindex(d::Probabilities{T, N, S}, args...) where {T, N, S} @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/probabilities.jl:97, eachindex(A::AbstractArray, B::AbstractArray...) @ Base abstractarray.jl:377)
 ((var"#ctor-self#"::Type{Outcome{T}} where T<:Integer)(num) @ ComplexityMeasures ~/Documents/Repos/ComplexityMeasures.jl/src/core/outcome.jl:14, (::Type{T})(x::AbstractChar) where T<:Union{AbstractChar, Number} @ Base char.jl:50)
kahaaga commented 5 months ago

The problem was that the generic @eval Base.$(f)(d::Probabilities{T, N, S}, args...) where {T, N, S} = $(f)(d.p, args...) with the extra args leads to a conflict with eachindex(A::AbstractArray, B::AbstractArray...) in base. The same is the case for Counts.

By excluding :eachindex from the list of symbols to be eval-ed and manually defining Base.eachindex(p::Probabilities) = eachindex(p.p), the issue is solved:

julia> c = counts(Dispersion(), rand(50))
 Counts{Int64,1} over 9 outcomes
 [1, 1]   9
 [1, 2]   3
 [1, 3]   7
 [2, 1]   3
 [2, 2]   2
 [2, 3]   3
 [3, 1]   7
 [3, 2]   3
 [3, 3]  12

julia> eachindex(c)
Base.OneTo(9)

julia> p = probabilities(Dispersion(), rand(50))
 Probabilities{Float64,1} over 9 outcomes
 [1, 1]  0.10204081632653061
 [1, 2]  0.14285714285714285
 [1, 3]  0.08163265306122448
 [2, 1]  0.10204081632653061
 [2, 2]  0.02040816326530612
 [2, 3]  0.10204081632653061
 [3, 1]  0.12244897959183673
 [3, 2]  0.061224489795918366
 [3, 3]  0.2653061224489796

julia> eachindex(p)
Base.OneTo(9)