CompatHelper: bump compat for StateSpaceSets to 2, (keep existing com…

…pat) (#426)

* CompatHelper: bump compat for StateSpaceSets to 2, (keep existing compat)

* use v2 exclusively

* bump project version

* fix approx entr argumenterror

* make `codify` do what was promised

* typing

* fix doc compat

* fix entropy sample problem (?)

* stop rounding 0 to 0...

* FIX DOCS ERROR

---------

Co-authored-by: CompatHelper Julia <compathelper_noreply@julialang.org>
Co-authored-by: Datseris <datseris.george@gmail.com>

CHANGELOG.md

@@ -2,7 +2,12 @@
 
 Changelog is kept with respect to version 0.11 of Entropies.jl. From version v2.0 onwards, this package has been renamed to ComplexityMeasures.jl.
 
-## 3.6 
+## 3.7
+
+- Updated to StateSpaceSets.jl v2.0
+- Fixed a bug in `codify` with `StateSpaceSet`. Now it does exactly as described in the docstring.
+
+## 3.6
 
 - New information measure: `FluctuationComplexity`.
 

Project.toml

@@ -2,7 +2,7 @@ name = "ComplexityMeasures"
 uuid = "ab4b797d-85ee-42ba-b621-05d793b346a2"
 authors = "Kristian Agasøster Haaga <kahaaga@gmail.com>, George Datseries <datseris.george@gmail.com>"
 repo = "https://github.com/juliadynamics/ComplexityMeasures.jl.git"
-version = "3.6.6"
+version = "3.7.0"
 
 [deps]
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
@@ -32,10 +32,10 @@ Neighborhood = "0.2.4"
 QuadGK = "2.9"
 Reexport = "1"
 SpecialFunctions = "0.10, 1.0, 2"
-StateSpaceSets = "1.0.4"
+StateSpaceSets = "2.1"
 StaticArrays = "0.12, 1.0"
 Statistics = "1"
 StatsBase = "0.33, 0.34"
 StatsFuns = "1.3"
-Wavelets = "0.10"
+Wavelets = "0.9, 0.10"
 julia = "1.5"

docs/Project.toml

@@ -23,4 +23,3 @@ TimeseriesSurrogates = "c804724b-8c18-5caa-8579-6025a0767c70"
 
 [compat]
 DynamicalSystemsBase = "3"
-StateSpaceSets = "1"

docs/src/examples.md

@@ -834,7 +834,7 @@ using CairoMakie
 N, a = 2000, 10
 t = LinRange(0, 2*a*π, N)
 
-x = repeat([-5:5 |> collect; 4:-1:-4 |> collect], N ÷ 20);
+x = repeat([-5:5.0 |> collect; 4.0:-1:-4 |> collect], N ÷ 20);
 y = sin.(t .+ cos.(t/0.5));
 z = rand(N)
 

src/complexity_measures/approximate_entropy.jl

@@ -82,7 +82,9 @@ Base.@kwdef struct ApproximateEntropy{I, B, R} <: ComplexityEstimator
     end
 end
 
-
+function complexity(c::ApproximateEntropy, x::AbstractStateSpaceSet)
+    throw(ArgumentError("Approximate entropy is only computable for timeseries."))
+end
 
 function complexity(c::ApproximateEntropy, x::AbstractVector{T}) where T
     (; m, τ, r, base) = c

src/core/encodings.jl

@@ -69,4 +69,6 @@ spaces preserve the input data length (e.g. [`UniqueElements`](@ref)), while
 some outcome spaces (e.g. [`OrdinalPatterns`](@ref)) do e.g. delay embeddings before
 encoding, so that `length(s) < length(x)`.
 """
-function codify end
+function codify(o::OutcomeSpace, s::AbstractStateSpaceSet)
+    return map(x -> codify(o, x), columns(s))
+end

src/outcome_spaces/cosine_similarity_binning.jl

@@ -50,8 +50,8 @@ An alias to [`CosineSimilarityBinning`](@ref).
 const Diversity = CosineSimilarityBinning
 
 function counts_and_outcomes(o::CosineSimilarityBinning, x::AbstractVector{T}) where T <: Real
-    # Cosine similarities are all on [-1.0, 1.0], so just discretize this interval. To 
-    # do so, we call the `counts_and_outcomes(::RectangularBinEncoding, x)` in the file 
+    # Cosine similarities are all on [-1.0, 1.0], so just discretize this interval. To
+    # do so, we call the `counts_and_outcomes(::RectangularBinEncoding, x)` in the file
     # `encoding_implementations/rectangular_binning.jl`.
     rbc::RectangularBinEncoding = encoding_for_diversity(o.nbins)
     cdists = cosine_similarity_distances(o, x)
@@ -87,7 +87,7 @@ function encoding_for_diversity(nbins::Int)
     return RectangularBinEncoding(binning)
 end
 
-function codify(o::CosineSimilarityBinning, x::AbstractVector{T}) where T
+function codify(o::CosineSimilarityBinning, x::AbstractVector{<:Real})
     τs = 0:o.τ:(o.m - 1)*o.τ
     Y = genembed(x, τs)
     ds = zeros(Float64, length(Y) - 1)

src/outcome_spaces/ordinal_patterns.jl

@@ -200,18 +200,18 @@ is_counting_based(o::AmplitudeAwareOrdinalPatterns) = false
 function OrdinalPatterns{m}(τ = 1, lt = isless_rand; kwargs...) where {m}
     if haskey(kwargs, :τ)
         msg = "Keyword argument `τ` to `OrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`OrdinalPatterns{m}(τ = 1, lt::Function = ComplexityMeasures.isless_rand)`" * 
-        ", so provide `τ` as a positional argument instead. " * 
+        "The signature is now " *
+        "`OrdinalPatterns{m}(τ = 1, lt::Function = ComplexityMeasures.isless_rand)`" *
+        ", so provide `τ` as a positional argument instead. " *
         "In this call, the given keyword `τ` is used instead of the positional `τ`."
         @warn msg
         τ = kwargs[:τ]
     end
     if haskey(kwargs, :lt)
         msg = "Keyword argument `lt` to `OrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`OrdinalPatterns{m}(τ = 1, lt::Function = ComplexityMeasures.isless_rand)`" * 
-        ", so provide `lt` as a positional argument instead. "  * 
+        "The signature is now " *
+        "`OrdinalPatterns{m}(τ = 1, lt::Function = ComplexityMeasures.isless_rand)`" *
+        ", so provide `lt` as a positional argument instead. "  *
         "In this call, the given keyword `lt` is used instead of the positional `lt`."
         @warn msg
         lt = kwargs[:lt]
@@ -225,18 +225,18 @@ end
 function WeightedOrdinalPatterns{m}(τ = 1, lt = isless_rand; kwargs...) where {m}
     if haskey(kwargs, :τ)
         msg = "Keyword argument `τ` to `WeightedOrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`WeightedOrdinalPatterns{m}(τ::Int = 1, lt::F=ComplexityMeasures.isless_rand)`" * 
-        ", so provide `τ` as a positional argument instead. "  * 
+        "The signature is now " *
+        "`WeightedOrdinalPatterns{m}(τ::Int = 1, lt::F=ComplexityMeasures.isless_rand)`" *
+        ", so provide `τ` as a positional argument instead. "  *
         "In this call, the given keyword `τ` is used instead of the positional `τ`."
         @warn msg
         τ = kwargs[:τ]
     end
     if haskey(kwargs, :lt)
         msg = "Keyword argument `lt` to `WeightedOrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`WeightedOrdinalPatterns{m}(τ = 1, lt::Function = ComplexityMeasures.isless_rand)`" * 
-        ", so provide `lt` as a positional argument instead. "  * 
+        "The signature is now " *
+        "`WeightedOrdinalPatterns{m}(τ = 1, lt::Function = ComplexityMeasures.isless_rand)`" *
+        ", so provide `lt` as a positional argument instead. "  *
         "In this call, the given keyword `lt` is used instead of the positional `lt`."
         @warn msg
         lt = kwargs[:lt]
@@ -248,38 +248,38 @@ function WeightedOrdinalPatterns{m}(τ = 1, lt = isless_rand; kwargs...) where {
     return WeightedOrdinalPatterns{m, F, I}(OrdinalPatternEncoding{m}(lt), τ)
 end
 
-function AmplitudeAwareOrdinalPatterns{m}(τ = 1, A = 0.5, lt = isless_rand; 
+function AmplitudeAwareOrdinalPatterns{m}(τ = 1, A = 0.5, lt = isless_rand;
         kwargs...) where {m}
     # because the order of the arguments is different from the other ordinal outcome spaces
     if A isa Function
         msg = "Second argument to `AmplitudeAwareOrdinalPatterns` must be a function. " *
         "Got a $(typeof(A)).";
         throw(ArgumentError(msg))
     end
-    
+
     if haskey(kwargs, :τ)
         msg = "Keyword argument `τ` to `AmplitudeAwareOrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`AmplitudeAwareOrdinalPatterns{m}(τ::Int = 1, A = 0.5, lt::F=isless_rand)`" * 
-        ", so provide `τ` as a positional argument instead. "  * 
+        "The signature is now " *
+        "`AmplitudeAwareOrdinalPatterns{m}(τ::Int = 1, A = 0.5, lt::F=isless_rand)`" *
+        ", so provide `τ` as a positional argument instead. "  *
         "In this call, the given keyword `τ` is used instead of the positional `τ`."
         @warn msg
         τ = kwargs[:τ]
     end
     if haskey(kwargs, :lt)
         msg = "Keyword argument `lt` to `AmplitudeAwareOrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`AmplitudeAwareOrdinalPatterns{m}(τ::Int = 1, A = 0.5, lt::F=isless_rand)`" * 
-        ", so provide `lt` as a positional argument instead. "  * 
+        "The signature is now " *
+        "`AmplitudeAwareOrdinalPatterns{m}(τ::Int = 1, A = 0.5, lt::F=isless_rand)`" *
+        ", so provide `lt` as a positional argument instead. "  *
         "In this call, the given keyword `lt` is used instead of the positional `lt`."
         @warn msg
         lt = kwargs[:lt]
     end
     if haskey(kwargs, :A)
         msg = "Keyword argument `A` to `AmplitudeAwareOrdinalPatterns` is deprecated. " *
-        "The signature is now " * 
-        "`AmplitudeAwareOrdinalPatterns{m}(τ::Int = 1, A = 0.5, lt::F=isless_rand)`" * 
-        ", so provide `A` as a positional argument instead. "  * 
+        "The signature is now " *
+        "`AmplitudeAwareOrdinalPatterns{m}(τ::Int = 1, A = 0.5, lt::F=isless_rand)`" *
+        ", so provide `A` as a positional argument instead. "  *
         "In this call, the given keyword `A` is used instead of the positional `A`."
         @warn msg
         A = kwargs[:A]
@@ -335,25 +335,9 @@ function fasthist!(πs::Vector{Int}, est::OrdinalOutcomeSpace{m}, x::AbstractSta
     return cts
 end
 
-function codify(est::OrdinalOutcomeSpace{m}, x) where m
-    if x isa AbstractVector
-        dataset = embed(x, m, est.τ)
-    elseif x isa AbstractStateSpaceSet && dimension(x) == 1
-        err = "Convert your univariate time series to a subtype of `AbstractVector` to " * 
-        "codify with ordinal patterns! A `StateSpaceSet` input is assumed to be " * 
-        "already  embedded in D = m >= 2 dimensional space."
-        throw(ArgumentError(err))
-    else
-        dataset = x
-    end
-    m != dimension(dataset) && throw(ArgumentError(
-        "Order of ordinal patterns and dimension of `StateSpaceSet` must match!"
-    ))
-    πs = zeros(Int, length(dataset))
-    @inbounds for (i, χ) in enumerate(dataset)
-        πs[i] = encode(est.encoding, χ)
-    end
-    return πs
+function codify(o::OrdinalOutcomeSpace{m}, x::AbstractVector{<:Real}) where {m}
+    emb = embed(x, m, o.τ).data
+    return encode.(Ref(o.encoding), emb)
 end
 
 # Special treatment for counting-based
@@ -448,8 +432,3 @@ Encode relative amplitude information of the elements of `a`.
 function AAPE(x, A::Real = 0.5, m::Int = length(x))
     (A/m)*sum(abs.(x)) + (1-A)/(m-1)*sum(abs.(diff(x)))
 end
-
-function codify(o::OrdinalPatterns{m}, x::AbstractVector) where {m}
-    emb = embed(x, m, o.τ).data
-    return encode.(Ref(o.encoding), emb)
-end

test/complexity/measures/entropy_sample.jl

@@ -1,20 +1,21 @@
-using ComplexityMeasures
+using ComplexityMeasures, Test
+
 @test_throws UndefKeywordError SampleEntropy()
 
 # Analytical examples seem to be lacking in the literature. As a next-best-test,
 # we just check that we get a sample entropy sufficiently close to zero for a completely
 # regular signal.
 N = 6000
 c = SampleEntropy(m = 2, τ = 1, r = 0.1)
-x = repeat([-5:5 |> collect; 4:-1:-4 |> collect], N ÷ 20);
-@test round(complexity(c, x), digits = 3) == round(0.0, digits = 3)
-@test round(complexity_normalized(c, x), digits = 3) == round(0.0, digits = 3)
+x = repeat([-5.0:5 |> collect; 4.0:-1:-4 |> collect], N ÷ 20);
+@test round(complexity(c, x), digits = 3) == 0.0
+@test round(complexity_normalized(c, x), digits = 3) == 0.0
 
 # Conversely, a non-regular signal should result in a sample entropy
 # greater than zero.
 x = rand(N)
-@test round(complexity(c, x), digits = 2) > round(0.0, digits = 2)
-@test round(complexity_normalized(c, x), digits = 2) > round(0.0, digits = 2)
+@test round(complexity(c, x), digits = 2) > 0.0
+@test round(complexity_normalized(c, x), digits = 2) > 0.0
 
 # Automatically deducing radius
-@test round(complexity(SampleEntropy(x, m = 2, τ = 1), x), digits = 2) > round(0.0, digits = 2)
+@test round(complexity(SampleEntropy(x, m = 2, τ = 1), x), digits = 2) > 0.0

test/outcome_spaces/implementations/permutation.jl

@@ -91,6 +91,11 @@ end
     WeightedOrdinalPatterns, AmplitudeAwareOrdinalPatterns)
     # Codification of vector inputs (time series)
     x = rand(30)
-    @test codify(S(), x) isa Vector{Int}
-    @test_throws ArgumentError codify(S(),StateSpaceSet(x))
+    y = rand(30)
+    s = StateSpaceSet(x, y)
+    c1 = codify(S(), x)
+    @test c1 isa Vector{Int}
+    out = codify(S(), s)
+    @test out isa Tuple
+    @test out[1] == c1
 end

test/outcome_spaces/implementations/unique_elements.jl

@@ -38,7 +38,7 @@ end
 
 # Codification of vector inputs (time series)
 x = [1, 3, 2, 1, 2, 2, 1, 3, 1]
-y = StateSpaceSet(["a", "b", "c", "b", "a"])
+y = ["a", "b", "c", "b", "a"]
 
 @test codify(UniqueElements(), x) isa Vector{Int}
 @test codify(UniqueElements(), y) isa Vector{Int}