work on mnist

notebooks/mnist.qmd

-
-
 ```{julia}
 include("notebooks/setup.jl")
 eval(setup_notebooks)
 ```
 
+# MNIST
+
 ```{julia}
 # Data:
-counterfactual_data = load_mnist()
+counterfactual_data = load_mnist(1000)
 X, y = CounterfactualExplanations.DataPreprocessing.unpack_data(counterfactual_data)
+X = table(permutedims(X))
 labels = counterfactual_data.output_encoder.labels
 input_dim, n_obs = size(counterfactual_data.X)
-M = load_mnist_mlp()
-
-# Target:
-factual_label = 8
-x = reshape(X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)
-target = 3
-factual = predict_label(M, counterfactual_data, x)[1]
-γ = 0.9
-T = 50
 ```
 
 ```{julia}
-builder = MLJFlux.@builder M.model
-clf = NeuralNetworkClassifier(builder=builder, epochs=100)
+epochs = 100
+clf = NeuralNetworkClassifier(builder=MLJFlux.MLP(hidden=(32,), σ=relu), epochs=epochs)
 conf_model = conformal_model(clf; method=:simple_inductive)
 mach = machine(conf_model, X, labels)
 fit!(mach)
 ```
 
 ```{julia}
-# Search:
+M = CCE.ConformalModel(mach.model, mach.fitresult)
+```
+
+```{julia}
+test_data = load_mnist_test()
+f1 = CounterfactualExplanations.Models.model_evaluation(M, test_data)
+println("F1 score (test): $(round(f1,digits=3))")
+```
+
+```{julia}
+# Set up search:
+factual_label = 8
+x = reshape(counterfactual_data.X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)
+target = 3
+factual = predict_label(M, counterfactual_data, x)[1]
+γ = 0.9
+T = 100
+
+# Generate counterfactual using generic generator:
 generator = GenericGenerator()
 ce_wachter = generate_counterfactual(
     x, target, counterfactual_data, M, generator; 
     decision_threshold=γ, max_iter=T,
     initialization=:identity,
 )
-generator = deepcopy(generator) |> gen -> @objective(gen, _ + 0.001distance + 1.0set_size_penalty)
+
+# Generate counterfactual using CCE generator:
+generator = CCEGenerator(λ=[0.0,10.0], temp=0.01, opt=CounterfactualExplanations.Generators.JSMADescent(η=5.0))
 ce_conformal = generate_counterfactual(
     x, target, counterfactual_data, M, generator; 
     decision_threshold=γ, max_iter=T,
     initialization=:identity,
+    converge_when=:generator_conditions,
 )
 ```
 
 ```{julia}
-p1 = plot(
+p1 = Plots.plot(
     convert2image(MNIST, reshape(x,28,28)),
     axis=nothing, 
     size=(img_height, img_height),
@@ -55,13 +68,13 @@ p1 = plot(
 )
 plts = [p1]
 
-ces = zip([ce_wachter,ce_jsma])
+ces = zip([ce_wachter,ce_conformal])
 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), eachslice(phat; dims=3))
     ce, _phat = (x[1],x[2])
     _title = "p(y=$(target)|x′)=$(round(_phat[1]; digits=3))"
-    plt = plot(
+    plt = Plots.plot(
         convert2image(MNIST, reshape(ce,28,28)),
         axis=nothing, 
         size=(img_height, img_height),
@@ -69,6 +82,7 @@ for x in zip(eachslice(counterfactuals; dims=3), eachslice(phat; dims=3))
     )
     plts = [plts..., plt]
 end
-plt = plot(plts...; size=(img_height*length(plts),img_height), layout=(1,length(plts)))
-savefig(plt, joinpath(www_path, "you_may_not_like_it.png"))
+plt = Plots.plot(plts...; size=(img_height*length(plts),img_height), layout=(1,length(plts)))
+display(plt)
+savefig(plt, joinpath(www_path, "cce_mnist.png"))
 ```
\ No newline at end of file