Rename MesnerShalisi to MesnerShalizi (#295)

JuliaDynamics · Apr 5, 2023 · 133feaa · 133feaa
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diff --git a/Project.toml b/Project.toml
@@ -2,7 +2,7 @@ name = "CausalityTools"
 uuid = "5520caf5-2dd7-5c5d-bfcb-a00e56ac49f7"
 authors = ["Kristian Agasøster Haaga <kahaaga@gmail.com>", "Tor Einar Møller <temolle@gmail.com>", "George Datseris <datseris.george@gmail.com>"]
 repo = "https://github.com/kahaaga/CausalityTools.jl.git"
-version = "2.3.0"
+version = "2.3.1"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

diff --git a/changelog.md b/changelog.md
@@ -1,5 +1,10 @@
 # Changelog
 
+## 2.3.1
+
+- The `MesnerShalisi` estimator is now deprecated and renamed to `MesnerShalizi` (with
+    correct spelling).
+
 ## 2.3.0
 
 - Significant speed-ups for `OCE` by sorting on maximal measure, thus avoiding

diff --git a/docs/src/api/api_condmutualinfo.md b/docs/src/api/api_condmutualinfo.md
@@ -35,10 +35,10 @@ GaussianCMI
 FPVP
 ```
 
-### [`MesnerShalisi`](@ref)
+### [`MesnerShalizi`](@ref)
 
 ```@docs
-MesnerShalisi
+MesnerShalizi
 ```
 
 ### [`PoczosSchneiderCMI`](@ref)

diff --git a/docs/src/examples/examples_conditional_mutual_information.md b/docs/src/examples/examples_conditional_mutual_information.md
@@ -42,7 +42,7 @@ z = (z ./ std(z)) .+ y
 condmutualinfo(FPVP(k = 5), x, z, y) # defaults to `CMIShannon()`
 ```
 
-#### [`CMIShannon`](@ref) with [`MesnerShalisi`](@ref)
+#### [`CMIShannon`](@ref) with [`MesnerShalizi`](@ref)
 
 ```@example mi_demonstration
 using CausalityTools
@@ -59,7 +59,7 @@ z = (z ./ std(z)) .+ y
 # We expect zero (in practice: very low) CMI when computing I(X; Z | Y), because
 # the link between X and Z is exclusively through Y, so when observing Y,
 # X and Z should appear independent.
-condmutualinfo(MesnerShalisi(k = 10), x, z, y) # defaults to `CMIShannon()`
+condmutualinfo(MesnerShalizi(k = 10), x, z, y) # defaults to `CMIShannon()`
 ```
 
 #### [`CMIShannon`](@ref) with [`Rahimzamani`](@ref)

diff --git a/docs/src/examples/examples_graphs.md b/docs/src/examples/examples_graphs.md
@@ -18,7 +18,7 @@ x, y, z, w = columns(trajectory(sys, 400, Ttr = 10000))
 
 # Independence tests for unconditional and conditional stages.
 utest = SurrogateTest(MIShannon(), KSG2(k = 3, w = 1); rng, nshuffles = 150)
-ctest = LocalPermutationTest(CMIShannon(), MesnerShalisi(k = 3, w = 1); rng, nshuffles = 150)
+ctest = LocalPermutationTest(CMIShannon(), MesnerShalizi(k = 3, w = 1); rng, nshuffles = 150)
 
 # Infer graph
 alg = OCE(; utest, ctest, α = 0.05, τmax = 1)

diff --git a/src/CausalityTools.jl b/src/CausalityTools.jl
@@ -56,19 +56,12 @@ module CausalityTools
     # Update messages:
     using Scratch
     display_update = true
-    version_number = "2.3.0"
+    version_number = "2.3.1"
     update_name = "update_v$(version_number)"
     update_message = """
     \nUpdate message: CausalityTools v$(version_number)\n
-    - Significant speed-ups for `OCE` by sorting on maximal measure, thus avoiding
-    unnecessary significance tests.
-    - Default parameters for `OCE` default lag parameter have changed. Now, `τmax = 1`, since
-        that is the only case considered in the original paper. We also use the
-        `MesnerShalisi` CMI estimator for the conditional step, because in contrast to
-        the `FPVP` estimator, it has been shown to be consistent.
-    - Source code for `OCE` has been drastically simplified by merging the pairwise
-        and conditional parent finding steps.
-    - `OCE` result can now be converted to a `SimpleDiGraph` from Graphs.jl.
+    - The `MesnerShalisi` estimator is now deprecated and renamed to `MesnerShalizi` (with
+    correct spelling).
     """
 
     if display_update

diff --git a/src/causal_graphs/oce/OCE.jl b/src/causal_graphs/oce/OCE.jl
@@ -6,7 +6,7 @@ export OCE
 """
     OCE <: GraphAlgorithm
     OCE(; utest::IndependenceTest = SurrogateTest(MIShannon(), KSG2(k = 3, w = 3)),
-          ctest::C = LocalPermutationTest(CMIShannon(), MesnerShalisi(k = 3, w = 3)),
+          ctest::C = LocalPermutationTest(CMIShannon(), MesnerShalizi(k = 3, w = 3)),
           τmax::T = 1, α = 0.05)
 
 The optimal causation entropy (OCE) algorithm for causal discovery (Sun et al.,
@@ -44,7 +44,7 @@ from Graphs.jl (see [example](@ref oce_example)).
 """
 Base.@kwdef struct OCE{U, C, T} <: GraphAlgorithm
     utest::U = SurrogateTest(MIShannon(), KSG2(k = 3, w = 3), nshuffles = 100)
-    ctest::C = LocalPermutationTest(CMIShannon(), MesnerShalisi(k = 3, w = 3), nshuffles = 100)
+    ctest::C = LocalPermutationTest(CMIShannon(), MesnerShalizi(k = 3, w = 3), nshuffles = 100)
     τmax::T = 1
     α = 0.05
 end

diff --git a/src/methods/infomeasures/condmutualinfo/condmutualinfo.jl b/src/methods/infomeasures/condmutualinfo/condmutualinfo.jl
@@ -27,7 +27,7 @@ The supertype of all conditional mutual information estimators.
 - [`FPVP`](@ref).
 - [`PoczosSchneiderCMI`](@ref).
 - [`Rahimzamani`](@ref).
-- [`MesnerShalisi`](@ref).
+- [`MesnerShalizi`](@ref).
 """
 abstract type ConditionalMutualInformationEstimator end
 const CMIEstimator = ConditionalMutualInformationEstimator
@@ -62,7 +62,7 @@ mixed.
 | Estimator                    | Principle         | [`CMIShannon`](@ref) | [`CMIRenyiPoczos`](@ref) |
 | ---------------------------- | ----------------- | :------------------: | :----------------------: |
 | [`FPVP`](@ref)               | Nearest neighbors |          ✓          |            x             |
-| [`MesnerShalisi`](@ref)      | Nearest neighbors |          ✓          |            x             |
+| [`MesnerShalizi`](@ref)      | Nearest neighbors |          ✓          |            x             |
 | [`Rahimzamani`](@ref)        | Nearest neighbors |          ✓          |            x             |
 | [`PoczosSchneiderCMI`](@ref) | Nearest neighbors |          x           |            ✓            |
 """

diff --git a/...ondmutualinfo/estimators/MesnerShalisi.jl → ...ondmutualinfo/estimators/MesnerShalizi.jl b/...ondmutualinfo/estimators/MesnerShalisi.jl → ...ondmutualinfo/estimators/MesnerShalizi.jl
@@ -1,25 +1,34 @@
+export MesnerShalizi
 export MesnerShalisi
-
 """
-    MesnerShalisi <: ConditionalMutualInformationEstimator
-    MesnerShalisi(k = 1, w = 0)
+    MesnerShalizi <: ConditionalMutualInformationEstimator
+    MesnerShalizi(k = 1, w = 0)
 
-The `MesnerShalisi` estimator is an estimator for conditional mutual information for data
+The `MesnerShalizi` estimator is an estimator for conditional mutual information for data
 that can be mixtures of discrete and continuous data (Mesner & Shalisi et al.,
-2020)[^MesnerShalisi2020].
+2020)[^MesnerShalizi2020].
 
-[^MesnerShalisi2020]:
+[^MesnerShalizi2020]:
     Mesner, O. C., & Shalizi, C. R. (2020). Conditional mutual information estimation for
     mixed, discrete and continuous data. IEEE Transactions on Information Theory, 67(1),
     464-484.
 """
-Base.@kwdef struct MesnerShalisi{M} <: ConditionalMutualInformationEstimator
+Base.@kwdef struct MesnerShalizi{M} <: ConditionalMutualInformationEstimator
     k::Int = 1
     w::Int = 0
     metric::M = Chebyshev()
 end
 
-function estimate(measure::CMIShannon, est::MesnerShalisi, x, y, z)
+function MesnerShalisi(args...; kwargs...)
+    Base.depwarn(
+        "The `MesnerShalisi` estimator is deprecated due to a type. " *
+        "Please use `MesnerShalizi` instead`.",
+        :MesnerShalisi,
+    )
+    return MesnerShalizi(args...; kwargs...)
+end
+
+function estimate(measure::CMIShannon, est::MesnerShalizi, x, y, z)
     e = measure.e
     (; k, w, metric) = est
     X = StateSpaceSet(x)

diff --git a/src/methods/infomeasures/condmutualinfo/estimators/estimators.jl b/src/methods/infomeasures/condmutualinfo/estimators/estimators.jl
@@ -1,7 +1,7 @@
 include("FPVP.jl")
 include("Rahimzamani.jl")
 include("PoczosSchneiderCMI.jl")
-include("MesnerShalisi.jl")
+include("MesnerShalizi.jl")
 include("GaussianCMI.jl")
 
 # Definition is actually never used, but we need to define it, so that calling `estimate`

diff --git a/src/methods/infomeasures/predictive_asymmetry/PA.jl b/src/methods/infomeasures/predictive_asymmetry/PA.jl
@@ -53,7 +53,7 @@ of the other dedicated conditional mutual information estimators.
 | [`GaoKannanOhViswanath`](@ref)   | [`MutualInformationEstimator`](@ref)            | Mixed               |    ✓    |     ✓      |
 | [`GaoOhViswanath`](@ref)         | [`MutualInformationEstimator`](@ref)            | Continuous          |    ✓    |     ✓      |
 | [`FPVP`](@ref)                   | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |    ✓    |     ✓      |
-| [`MesnerShalisi`](@ref)          | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |    ✓    |     ✓      |
+| [`MesnerShalizi`](@ref)          | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |    ✓    |     ✓      |
 | [`Rahimzamani`](@ref)            | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |    ✓    |     ✓      |
 
 ## Examples

diff --git a/src/methods/infomeasures/transferentropy/TEShannon.jl b/src/methods/infomeasures/transferentropy/TEShannon.jl
@@ -54,7 +54,7 @@ which all boil down to computing conditional mutual information, except for
 | [`GaoKannanOhViswanath`](@ref)   | [`MutualInformationEstimator`](@ref)            | Mixed               |         ✓          |
 | [`GaoOhViswanath`](@ref)         | [`MutualInformationEstimator`](@ref)            | Continuous          |         ✓          |
 | [`FPVP`](@ref)                   | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |
-| [`MesnerShalisi`](@ref)          | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |
+| [`MesnerShalizi`](@ref)          | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |
 | [`Rahimzamani`](@ref)            | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |
 | [`Zhu1`](@ref)                   | [`TransferEntropyEstimator`](@ref)              | Nearest neighbors   |         ✓          |
 | [`Lindner`](@ref)                | [`TransferEntropyEstimator`](@ref)              | Nearest neighbors   |         ✓          |

diff --git a/src/methods/infomeasures/transferentropy/transferentropy.jl b/src/methods/infomeasures/transferentropy/transferentropy.jl
@@ -92,7 +92,7 @@ exception - they compute transfer entropy directly.
 | [`GaoKannanOhViswanath`](@ref)   | [`MutualInformationEstimator`](@ref)            | Mixed               |         ✓          |           ✖            |
 | [`GaoOhViswanath`](@ref)         | [`MutualInformationEstimator`](@ref)            | Continuous          |         ✓          |           ✖            |
 | [`FPVP`](@ref)                   | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |           ✖            |
-| [`MesnerShalisi`](@ref)          | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |           ✖            |
+| [`MesnerShalizi`](@ref)          | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |           ✖            |
 | [`Rahimzamani`](@ref)            | [`ConditionalMutualInformationEstimator`](@ref) | Nearest neighbors   |         ✓          |           ✖            |
 | [`Zhu1`](@ref)                   | [`TransferEntropyEstimator`](@ref)              | Nearest neighbors   |         ✓          |           ✖            |
 | [`Lindner`](@ref)                | [`TransferEntropyEstimator`](@ref)              | Nearest neighbors   |         ✓          |           ✖            |

diff --git a/test/causal_graphs/oce.jl b/test/causal_graphs/oce.jl
@@ -8,7 +8,7 @@ rng = StableRNG(123)
 sys = system(Logistic4Chain(; rng))
 X = columns(trajectory(sys, 60, Ttr = 10000))
 utest = SurrogateTest(MIShannon(), KSG1(k = 10, w = 1); rng, nshuffles = 30)
-ctest = LocalPermutationTest(CMIShannon(), MesnerShalisi(k = 10, w = 1); rng, nshuffles = 30)
+ctest = LocalPermutationTest(CMIShannon(), MesnerShalizi(k = 10, w = 1); rng, nshuffles = 30)
 alg = OCE(; utest, ctest, τmax = 2)
 parents = infer_graph(alg, X; verbose = true)
 @test parents isa Vector{<:OCESelectedParents}
@@ -18,7 +18,7 @@ rng = StableRNG(123)
 sys = system(Logistic2Bidir(; rng))
 X = columns(trajectory(sys, 300, Ttr = 10000))
 utest = SurrogateTest(MIShannon(), KSG1(k = 10, w = 1); rng, nshuffles = 100)
-ctest = LocalPermutationTest(CMIShannon(), MesnerShalisi(k = 10, w = 1); rng, nshuffles = 100)
+ctest = LocalPermutationTest(CMIShannon(), MesnerShalizi(k = 10, w = 1); rng, nshuffles = 100)
 parents = infer_graph(OCE(; utest, ctest, τmax = 1), X; verbose = true)
 @test parents isa Vector{<:OCESelectedParents}
 g = SimpleDiGraph(parents)

diff --git a/test/methods/infomeasures/conditional_mutualinfo/CMIShannon.jl b/test/methods/infomeasures/conditional_mutualinfo/CMIShannon.jl
@@ -36,12 +36,13 @@ y = StateSpaceSet(rand(1000, 1))
 z = StateSpaceSet(rand(1000, 1))
 
 @test FPVP() isa FPVP
-@test MesnerShalisi() isa MesnerShalisi
+@test MesnerShalisi() isa MesnerShalizi
+@test MesnerShalizi() isa MesnerShalizi
 @test PoczosSchneiderCMI() isa PoczosSchneiderCMI
 @test Rahimzamani() isa Rahimzamani
 @test GaussianCMI() isa GaussianCMI
 @test condmutualinfo(FPVP(), x, y, z) isa Real
-@test condmutualinfo(MesnerShalisi(), x, y, z) isa Real
+@test condmutualinfo(MesnerShalizi(), x, y, z) isa Real
 @test condmutualinfo(PoczosSchneiderCMI(), x, y, z) isa Real
 @test condmutualinfo(Rahimzamani(), x, y, z) isa Real
 @test condmutualinfo(GaussianCMI(), x, y, z) isa Real