reg strength

experiments/benchmarking/benchmarking.jl

@@ -6,6 +6,7 @@ function default_generators(;
     opt=Flux.Optimise.Descent(0.01),
     nsamples::Union{Nothing,Int}=nothing,
     nmin::Union{Nothing,Int}=nothing,
+    reg_strength::Real=0.5,
 )
 
     @info "Begin benchmarking counterfactual explanations."
@@ -20,9 +21,9 @@ function default_generators(;
             "ECCCo" => ECCCoGenerator(λ=Λ, opt=opt, use_class_loss=use_class_loss, nsamples=nsamples, nmin=nmin),
             "ECCCo (no CP)" => ECCCoGenerator(λ=[λ₁, 0.0, λ₃], opt=opt, use_class_loss=use_class_loss, nsamples=nsamples, nmin=nmin),
             "ECCCo (no EBM)" => ECCCoGenerator(λ=[λ₁, λ₂, 0.0], opt=opt, use_class_loss=use_class_loss, nsamples=nsamples, nmin=nmin),
-            "ECCCo-Δ" => ECCCoGenerator(λ=Λ_Δ, opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin),
-            "ECCCo-Δ (no CP)" => ECCCoGenerator(λ=[λ₁_Δ, 0.0, λ₃_Δ], opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin),
-            "ECCCo-Δ (no EBM)" => ECCCoGenerator(λ=[λ₁_Δ, λ₂_Δ, 0.0], opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin),
+            "ECCCo-Δ" => ECCCoGenerator(λ=Λ_Δ, opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin, reg_strength=reg_strength),
+            "ECCCo-Δ (no CP)" => ECCCoGenerator(λ=[λ₁_Δ, 0.0, λ₃_Δ], opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin, reg_strength=reg_strength),
+            "ECCCo-Δ (no EBM)" => ECCCoGenerator(λ=[λ₁_Δ, λ₂_Δ, 0.0], opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin, reg_strength=reg_strength),
         )
     else
         generator_dict = Dict(
@@ -30,7 +31,7 @@ function default_generators(;
             "REVISE" => REVISEGenerator(λ=λ₁, opt=opt),
             "Schut" => GreedyGenerator(),
             "ECCCo" => ECCCoGenerator(λ=Λ, opt=opt, use_class_loss=use_class_loss, nsamples=nsamples, nmin=nmin),
-            "ECCCo-Δ" => ECCCoGenerator(λ=Λ_Δ, opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin),
+            "ECCCo-Δ" => ECCCoGenerator(λ=Λ_Δ, opt=opt, use_class_loss=use_class_loss, use_energy_delta=true, nsamples=nsamples, nmin=nmin, reg_strength=reg_strength),
         )
     end
     return generator_dict
@@ -63,6 +64,7 @@ function run_benchmark(exper::Experiment, model_dict::Dict)
             opt=exper.opt,
             nsamples=exper.nsamples,
             nmin=exper.nmin,
+            reg_strength=exper.reg_strength,
         )
     end
 

experiments/credit_default.jl

+counterfactual_data, test_data = train_test_split(load_credit_default(nothing); test_size=TEST_SIZE)
+
+# Default builder:
+n_hidden = 128
+activation = Flux.swish
+builder = MLJFlux.@builder Flux.Chain(
+    Dense(n_in, n_hidden, activation),
+    Dense(n_hidden, n_hidden, activation),
+    Dense(n_hidden, n_out),
+)
+
+# Number of individuals:
+n_ind = N_IND_SPECIFIED ? N_IND : 10
+
+run_experiment(
+    counterfactual_data, test_data;
+    dataname="Credit Default",
+    builder=builder,
+    α=[1.0, 1.0, 1e-1],
+    sampling_batch_size=10,
+    sampling_steps=30,
+    use_ensembling=true,
+    Λ=[0.1, 0.5, 0.5],
+    opt=Flux.Optimise.Descent(0.05),
+    n_individuals=n_ind,
+    use_variants=false
+)
\ No newline at end of file

experiments/experiment.jl

@@ -36,6 +36,7 @@ Base.@kwdef struct Experiment
     finaliser::Function = Flux.softmax
     loss::Function = Flux.Losses.crossentropy
     train_parallel::Bool = false
+    reg_strength::Real = 0.5
 end
 
 "A container to hold the results of an experiment."

experiments/synthetic.sh

@@ -11,4 +11,4 @@
 
 module load 2023r1 openmpi
 
-srun julia --project=experiments experiments/run_experiments.jl -- data=linearly_separable,moons,circles output_path=results retrain threaded mpi > experiments/synthetic.log
+srun julia --project=experiments --threads 4 experiments/run_experiments.jl -- data=linearly_separable,moons,circles output_path=results retrain threaded mpi > experiments/synthetic.log

src/generator.jl

@@ -11,6 +11,7 @@ function ECCCoGenerator(;
     use_energy_delta::Bool=false,
     nsamples::Union{Nothing,Int}=nothing,
     nmin::Union{Nothing,Int}=nothing,
+    reg_strength::Real=0.5,
     kwargs...
 )
 
@@ -30,7 +31,7 @@ function ECCCoGenerator(;
     end
 
     _energy_penalty =
-        use_energy_delta ? (ECCCo.energy_delta, (n=nsamples, nmin=nmin)) : (ECCCo.distance_from_energy, (n=nsamples, nmin=nmin))
+        use_energy_delta ? (ECCCo.energy_delta, (n=nsamples, nmin=nmin, reg_strength=reg_strength)) : (ECCCo.distance_from_energy, (n=nsamples, nmin=nmin))
 
     _penalties = [
         (Objectives.distance_l1, []), 

src/penalties.jl

@@ -46,10 +46,10 @@ function energy_delta(
     choose_random=false,
     nmin::Int=25,
     return_conditionals=false,
+    reg_strength=0.5,
     kwargs...
 )
 
-    _loss = 0.0
     nmin = minimum([nmin, n])
 
     @assert choose_lowest_energy ⊻ choose_random || !choose_lowest_energy && !choose_random "Must choose either lowest energy or random samples or neither."
@@ -88,7 +88,7 @@ function energy_delta(
     if return_conditionals
         return conditional_samples[1]
     end
-    return gen_loss + 0.1reg_loss
+    return gen_loss + reg_strength * reg_loss
 
 end