bloody jupyter seriously

notebooks/mnist.qmd

@@ -21,7 +21,7 @@ clf = NeuralNetworkClassifier(
     epochs=epochs,
     batch_size=Int(round(n_obs/10))
 )
-conf_model = conformal_model(clf; method=:simple_inductive, coverage=.99)
+conf_model = conformal_model(clf; method=:adaptive_inductive, coverage=.99)
 mach = machine(conf_model, X, labels)
 fit!(mach)
 ```
@@ -44,9 +44,9 @@ dt_reduced = counterfactual_data
 
 ```{julia}
 # Set up search:
-factual_label = 9
+factual_label = 8
 x = reshape(counterfactual_data.X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)
-target = 4
+target = 3
 factual = predict_label(M, counterfactual_data, x)[1]
 γ = 0.9
 T = 100
@@ -61,7 +61,7 @@ ce_wachter = generate_counterfactual(
 
 # Generate counterfactual using CCE generator:
 generator = CCEGenerator(
-    λ=[0.0,10.0], 
+    λ=[0.0,100.0], 
     temp=0.01, 
     # opt=CounterfactualExplanations.Generators.JSMADescent(η=5.0),
 )
@@ -71,9 +71,8 @@ ce_conformal = generate_counterfactual(
     initialization=:identity,
     converge_when=:generator_conditions,
 )
-```
 
-```{julia}
+# Plot:
 p1 = Plots.plot(
     convert2image(MNIST, reshape(x,28,28)),
     axis=nothing,