:sob

notebooks/mnist.qmd

@@ -79,7 +79,7 @@ mlp_ens = EnsembleModel(model=mlp, n=5)
 
 ```{julia}
 cov = .90
-conf_model = conformal_model(mlp; method=:adaptive_inductive, coverage=cov)
+conf_model = conformal_model(jem; method=:adaptive_inductive, coverage=cov)
 mach = machine(conf_model, X, labels)
 fit!(mach)
 M = CCE.ConformalModel(mach.model, mach.fitresult)
@@ -115,32 +115,40 @@ println("F1 score (test): $(round(f1,digits=3))")
 ```
 
 ```{julia}
-Random.seed!(1234)
+# Random.seed!(1234)
 
 # Set up search:
-factual_label = 4
+factual_label = 8
 x = reshape(counterfactual_data.X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)
-target = 9
+target = 3
 factual = predict_label(M, counterfactual_data, x)[1]
 γ = 0.5
 T = 100
 
 # Generate counterfactual using generic generator:
-generator = GenericGenerator()
+generator = GenericGenerator(opt=Flux.Optimise.Adam(),)
 ce_wachter = generate_counterfactual(
     x, target, counterfactual_data, M, generator; 
     decision_threshold=γ, max_iter=T,
     initialization=:identity,
 )
 
+generator = GreedyGenerator(η=1.0)
+ce_jsma = generate_counterfactual(
+    x, target, counterfactual_data, M, generator; 
+    decision_threshold=γ, max_iter=T,
+    initialization=:identity,
+)
+
 # CCE:
 λ=[0.0,1.0]
-temp=0.5
+temp=0.01
 
 # Generate counterfactual using CCE generator:
 generator = CCEGenerator(
     λ=λ, 
     temp=temp, 
+    opt=Flux.Optimise.Adam(),
 )
 ce_conformal = generate_counterfactual(
     x, target, counterfactual_data, M, generator; 
@@ -171,15 +179,15 @@ p1 = Plots.plot(
 )
 plts = [p1]
 
-ces = zip([ce_wachter,ce_conformal,ce_conformal_jsma])
-_names = ["Wachter", "CCE", "CCE-JSMA"]
-counterfactuals = reduce((x,y)->cat(x,y,dims=3),map(ce -> CounterfactualExplanations.counterfactual(ce[1]), ces))
-phat = reduce((x,y) -> cat(x,y,dims=3), map(ce -> target_probs(ce[1]), ces))
-for x in zip(eachslice(counterfactuals; dims=3), _names, eachslice(phat; dims=3))
-    ce, _name, _phat = (x[1],x[2],x[3])
+ces = [ce_wachter, ce_conformal, ce_jsma, ce_conformal_jsma]
+_names = ["Wachter", "CCE", "JSMA", "CCE-JSMA"]
+for x in zip(ces, _names)
+    ce, _name = (x[1],x[2])
+    x = CounterfactualExplanations.counterfactual(ce)
+    _phat = target_probs(ce)
     _title = "$_name (p̂=$(round(_phat[1]; digits=3)))"
     plt = Plots.plot(
-        convert2image(MNIST, reshape(ce,28,28)),
+        convert2image(MNIST, reshape(x,28,28)),
         axis=nothing, 
         size=(img_height, img_height),
         title=_title