anonymous functions causing some trouble with serialization

experiments/Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.9.3"
 manifest_format = "2.0"
-project_hash = "d02f2655e27de24f6692fd688a43f5d4a28ee28f"
+project_hash = "6d2faa27f9fe437fc49cbf54483df3f91bbcd105"
 
 [[deps.AbstractFFTs]]
 deps = ["LinearAlgebra"]
@@ -422,7 +422,7 @@ version = "2.2.1"
 
 [[deps.ConformalPrediction]]
 deps = ["CategoricalArrays", "ChainRules", "ComputationalResources", "Flux", "LazyArtifacts", "LinearAlgebra", "MLJBase", "MLJEnsembles", "MLJFlux", "MLJModelInterface", "MLUtils", "NaturalSort", "Plots", "ProgressMeter", "Random", "Serialization", "StatsBase", "Tables"]
-path = "../../ConformalPrediction.jl"
+git-tree-sha1 = "bd6c9678a6b98c024e2b615c9b04f9173f4feadf"
 uuid = "98bfc277-1877-43dc-819b-a3e38c30242f"
 version = "0.1.9"
 
@@ -450,7 +450,7 @@ version = "0.6.2"
 
 [[deps.CounterfactualExplanations]]
 deps = ["CSV", "CUDA", "CategoricalArrays", "Chain", "ChainRulesCore", "DataFrames", "DecisionTree", "Distributions", "EvoTrees", "Flux", "LaplaceRedux", "LazyArtifacts", "LinearAlgebra", "MLDatasets", "MLJBase", "MLJDecisionTreeInterface", "MLJModels", "MLUtils", "MultivariateStats", "NearestNeighborModels", "PackageExtensionCompat", "Parameters", "Plots", "ProgressMeter", "Random", "Serialization", "SnoopPrecompile", "Statistics", "StatsBase", "Tables", "UMAP"]
-path = "../../CounterfactualExplanations.jl"
+git-tree-sha1 = "8393721ffa3c9be209a93eb154d0d9fe9ca187d5"
 uuid = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
 version = "0.1.15"
 

experiments/Project.toml

@@ -10,6 +10,7 @@ ECCCo = "0232c203-4013-4b0d-ad96-43e3e11ac3bf"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 JointEnergyModels = "48c56d24-211d-4463-bbc0-7a701b291131"
 LazyArtifacts = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJEnsembles = "50ed68f4-41fd-4504-931a-ed422449fee0"
 MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"

experiments/run_experiments.jl

-# Parallelization:
-if "parallel" ∈ ARGS
-    @info "Running benchmarks in parallel."
-    import MPI
-    MPI.Init()
-    const PARALLEL = true
-    const PLZ = MPIParallelizer(MPI.COMM_WORLD)
-else
-    @info "Running benchmarks sequentially."
-    const PARALLEL = false
-    const PLZ = nothing
-end
-
 include("setup_env.jl");
 include("experiment.jl");
 
@@ -20,7 +7,6 @@ if "run-all" in ARGS
 else
     datanames = [ARGS[findall(contains.(ARGS, "data="))] |> x -> replace(x, "data=" => "")]
 end
-datanames = ["linearly_separable", "moons", "circles", "mnist", "gmsc"]
 
 # Linearly Separable
 if "linearly_separable" in datanames

experiments/setup_env.jl

@@ -17,12 +17,30 @@ using ECCCo
 using Flux
 using JointEnergyModels
 using LazyArtifacts
+using Logging
 using MLJBase: multiclass_f1score, accuracy, multiclass_precision, table, machine, fit!
 using MLJEnsembles
 using MLJFlux
 using Serialization
 using TidierData
 
+# Parallelization:
+if "parallel" ∈ ARGS
+    @info "Running benchmarks in parallel."
+    import MPI
+    MPI.Init()
+    const PARALLEL = true
+    const PLZ = MPIParallelizer(MPI.COMM_WORLD)
+    if MPI.Comm_rank(MPI.COMM_WORLD) != 0
+        @info "Disabling logging on non-root processes."
+        global_logger(NullLogger())
+    end
+else
+    @info "Running benchmarks sequentially."
+    const PARALLEL = false
+    const PLZ = nothing
+end
+
 # Constants:
 const LATEST_VERSION = "1.8.5"
 const ARTIFACT_NAME = "results-paper-submission-$(LATEST_VERSION)"

src/generator.jl

@@ -42,12 +42,10 @@ function ECCCoGenerator(;
     end
 
     # Set size penalty
-    function _set_size_penalty(ce::AbstractCounterfactualExplanation)
-        return ECCCo.set_size_penalty(ce; κ=κ, temp=temp)
-    end
+    _set_size_penalty = (ce::AbstractCounterfactualExplanation) -> ECCCo.set_size_penalty(ce; κ=κ, temp=temp)
 
     # Energy penalty
-    function _energy_penalty(ce::AbstractCounterfactualExplanation)
+    _energy_penalty = function (ce::AbstractCounterfactualExplanation)
         if use_energy_delta
             return ECCCo.energy_delta(ce; n=nsamples, nmin=nmin, kwargs...)
         else