synthetic

experiments/circles.jl

@@ -7,7 +7,9 @@ run_experiment(
     n_hidden=32,
     α=[1.0, 1.0, 1e-2],
     sampling_steps=20,
-    Λ=[0.1, 0.1, 0.1],
     opt=Flux.Optimise.Descent(0.01),
     activation=Flux.swish,
+    Λ=[0.1, 0.2, 0.2],
+    nsamples=100,
+    niter_eccco=100,
 )
\ No newline at end of file

experiments/german_credit.sh → experiments/jobscripts/counterfactuals/german_credit.sh

@@ -11,4 +11,4 @@
 
 module load 2023r1 openmpi
 
-srun julia --project=experiments experiments/run_experiments.jl -- data=german_credit output_path=results retrain mpi > experiments/german_credit.log
+srun julia --project=experiments experiments/run_experiments.jl -- data=german_credit output_path=results mpi > experiments/german_credit.log

experiments/mnist.sh → experiments/jobscripts/counterfactuals/mnist.sh

@@ -11,4 +11,4 @@
 
 module load 2023r1 openmpi
 
-srun julia --project=experiments experiments/run_experiments.jl -- data=mnist output_path=results mpi retrain > experiments/mnist.log
+srun julia --project=experiments experiments/run_experiments.jl -- data=mnist output_path=results mpi > experiments/mnist.log

experiments/linearly_separable.jl

@@ -7,6 +7,6 @@ run_experiment(
     counterfactual_data, test_data; 
     dataname="Linearly Separable",
     nsamples=100,
-    nmin=1,
-    niter_eccco=30
+    niter_eccco=100,
+    Λ=[0.1, 0.2, 0.2],
 )
\ No newline at end of file

experiments/moons.jl

@@ -8,7 +8,9 @@ run_experiment(
     activation = Flux.relu,
     sampling_batch_size=10,
     sampling_steps=30,
-    Λ=[0.1, 0.1, 0.1],
     opt=Flux.Optimise.Descent(0.05),
-    α=[1.0, 1.0, 1e-1]
+    α=[1.0, 1.0, 1e-1],
+    nsamples=100,
+    niter_eccco=100,
+    Λ = [0.1, 0.2, 0.2],
 )
\ No newline at end of file

src/penalties.jl

@@ -50,27 +50,6 @@ function energy_delta(
     kwargs...
 )
 
-    # 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."
-
-    # conditional_samples = []
-    # ignore_derivatives() do
-    #     _dict = ce.params
-    #     if !(:energy_sampler ∈ collect(keys(_dict)))
-    #         _dict[:energy_sampler] = ECCCo.EnergySampler(ce; niter=niter, nsamples=n, kwargs...)
-    #     end
-    #     eng_sampler = _dict[:energy_sampler]
-    #     if choose_lowest_energy
-    #         nmin = minimum([nmin, size(eng_sampler.buffer)[end]])
-    #         xmin = ECCCo.get_lowest_energy_sample(eng_sampler; n=nmin)
-    #         push!(conditional_samples, xmin)
-    #     elseif choose_random
-    #         push!(conditional_samples, rand(eng_sampler, n; from_buffer=from_buffer))
-    #     else
-    #         push!(conditional_samples, eng_sampler.buffer)
-    #     end
-    # end
     conditional_samples = []
     ignore_derivatives() do 
         _dict = ce.params
@@ -85,7 +64,7 @@ function energy_delta(
     xgenerated = conditional_samples[1]                         # conditional samples
     xproposed = CounterfactualExplanations.decode_state(ce)     # current state
     t = get_target_index(ce.data.y_levels, ce.target)
-    E(x) = -logits(ce.M, x)[t,:]                                # negative logits for target class
+    E(x) = -logits(ce.M, x)[t,:]                                # negative logits for taraget class
 
     # Generative loss:
     gen_loss = E(xproposed) .- E(xgenerated)