all coding stuff done

notebooks/mnist.qmd

@@ -165,7 +165,7 @@ end
 ```{julia}
 # Hyper:
 _retrain = false
-_regen = true
+_regen = false
 
 # Data:
 n_obs = 10000
@@ -369,12 +369,16 @@ function _plot_eccco_mnist(
     model_dict=model_dict,
     wide::Bool = false,
     img_height::Int = img_height,
+    plot_factual::Bool = false,
+    kwrgs...,
 )
 
     # Setup:
     Random.seed!(rng)
     if x isa Int
-        x = reshape(counterfactual_data.X[:,rand(findall(labels.==x))],input_dim,1)
+        x_fact = counterfactual_data.X[:,rand(findall(labels.==x))][:,:]
+    else
+        x_fact = x
     end
 
     # Generate counterfactuals using ECCCo generator:
@@ -390,7 +394,7 @@ function _plot_eccco_mnist(
     ces = Dict()
     for (mod_name, mod) in model_dict
         ce = generate_counterfactual(
-            x, target, counterfactual_data, mod, eccco_generator; 
+            x_fact, target, counterfactual_data, mod, eccco_generator; 
             decision_threshold=γ, max_iter=T,
             initialization=:identity,
             converge_when=:generator_conditions,
@@ -401,13 +405,17 @@ function _plot_eccco_mnist(
 
     # Plot:
     p1 = Plots.plot(
-        convert2image(MNIST, reshape(x,28,28)),
-        axis=nothing, 
+        convert2image(MNIST, reshape(x_fact,28,28)),
+        axis=([], false),
         size=(img_height, img_height),
         title="Factual"
     )
 
-    plts = []
+    if plot_factual
+        plts = [p1]
+    else
+        plts = []
+    end
     letters = collect('a':'z')[1:length(ces)]
     _count = 1
 
@@ -425,9 +433,9 @@ function _plot_eccco_mnist(
         _count += 1
     end
     if wide
-        plt = Plots.plot(plts...; size=(img_height*length(plts),img_height), layout=(1,length(plts)))
+        plt = Plots.plot(plts...; size=(img_height*length(plts),img_height), layout=(1,length(plts)), kwrgs...)
     else
-        plt = Plots.plot(plts...; size=(img_height,img_height))
+        plt = Plots.plot(plts...; size=(img_height,img_height), kwrgs...)
     end
     
     return plt, eccco_generator, ces
@@ -505,7 +513,7 @@ end
 
 function MLJFlux.build(b::RobustNetBuilder, rng, n_in, n_out)
 	n_hidden, γ = b.n_hidden, b.lipschitz_bound
-    _n_hidden = fill(n_hidden,1)
+    _n_hidden = fill(n_hidden,2)
     model_ps = DenseLBDNParams{Float32}(n_in, _n_hidden, n_out, γ; rng)
     chain = Flux.Chain(DiffLBDN(model_ps))
 	return chain
@@ -513,8 +521,8 @@ end
 
 # Final model:
 rob_net = NeuralNetworkClassifier(
-    builder=RobustNetBuilder(128, 5.0),
-    epochs=epochs,
+    builder=RobustNetBuilder(60, 5.0),
+    epochs=600,
     batch_size=batch_size,
     finaliser=_finaliser,
     loss=_loss,
@@ -581,11 +589,39 @@ plt_additional_models, _, _ces_ = _plot_eccco_mnist(
     plt_order = _plt_order,
     model_dict=large_model_dict,
     wide = true,
+    plot_factual = true,
+    img_height = 150,
 )
 display(plt_additional_models)
 savefig(plt_additional_models, joinpath(output_images_path, "mnist_eccco_additional.png"))
 ```
 
+```{julia}
+combos = [
+    (7,2),
+    (0,8),
+    (8,3),
+    (9,4),
+]
+
+plts = [plt_additional_models]
+
+for (factual, target) in combos
+    plt, _, _ = _plot_eccco_mnist(
+        plt_order = _plt_order,
+        model_dict = large_model_dict,
+        x = factual,
+        target = target,
+        wide = true,
+        plot_factual = true,
+        img_height = 150
+    )
+    display(plt)
+    push!(plts, plt)
+end
+```
+
+
 ### All digits
 
 ```{julia}