error accessing learning rate of composed optimisers

experiments/benchmarking/benchmarking.jl

@@ -20,7 +20,7 @@ function default_generators(;
         generator_dict = Dict(
             "Wachter" => WachterGenerator(λ = λ₁, opt = opt),
             "REVISE" => REVISEGenerator(λ = λ₁, opt = opt),
-            "Schut" => GreedyGenerator(η = opt.eta),
+            "Schut" => GreedyGenerator(η=get_learning_rate(opt)),
             "ECCCo" => ECCCoGenerator(
                 λ = Λ,
                 opt = opt,

experiments/grid_search.jl

@@ -38,7 +38,8 @@ function grid_search(
         @info "Running experiment $(counter)/$(length(grid)) with tuning parameters: $(params)"
 
         # Filter out keyword parameters that are tuned:
-        not_these = keys(kwargs)[findall([k in map(k -> k[1], params) for k in keys(kwargs)])]
+        _keys = [k[1] for k in kwargs]
+        not_these = _keys[findall([k in map(k -> k[1], params) for k in _keys])]
         not_these = (not_these..., :n_individuals)
         kwargs = filter(x -> !(x[1] ∈ not_these), kwargs)
 

experiments/post_processing/meta_data.jl

@@ -83,7 +83,7 @@ function meta_generators(
     generator_params = DataFrame(
         Dict(
             :opt => string(typeof(opt)),
-            :eta => opt.eta,
+            :eta => get_learning_rate(opt),
             :dataname => exper.dataname,
             :lambda_1 => string(Λ[1]),
             :lambda_2 => string(Λ[2]),

experiments/utils.jl

 using CounterfactualExplanations.Parallelization: ThreadsParallelizer
+using Flux
 using LinearAlgebra: norm
 
 function is_multi_processed(parallelizer::Union{Nothing,AbstractParallelizer})
@@ -25,3 +26,15 @@ function standardize(x::AbstractArray)
     x_norm = replace(x_norm, NaN => 0.0)
     return x_norm
 end
+
+function get_learning_rate(opt::Flux.Optimise.AbstractOptimiser)
+    if hasfield(typeof(opt), :eta)
+        return opt.eta
+    elseif hasfield(typeof(opt), :os)
+        _os = opt.os
+        opt = _os[findall([:eta in fieldnames(typeof(o)) for o in _os])][1]
+        return opt.eta
+    else 
+        throw(ArgumentError("Cannot find learning rate."))
+    end
+end
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