fuck em

experiments/post_processing/plotting.jl

@@ -62,21 +62,15 @@ function plot_random_eccco(outcome::ExperimentOutcome; generator="ECCCo-Δ", img
 
     # Get output:
     bmk = outcome.bmk()
-    grouped_bmk = groupby(bmk[bmk.variable.=="distance" .&& bmk.generator.==generator, :], [:dataname, :target, :factual])
-    random_choice = rand(1:length(grouped_bmk))
-    models = unique(bmk.model)
-    n_models = length(models)
-
-    df = grouped_bmk[random_choice]
-    while nrow(df) > n_models
-        random_choice = rand(1:length(grouped_bmk))
-        df = grouped_bmk[random_choice]
-    end
-    sort!(df, :model)
-    models = df.model
+    ce = rand(bmk.ce)
+    gen = outcome.generator_dict[generator]
+    models = outcome.model_dict
+    x = CounterfactualExplanations.counterfactual(ce)
+    target = ce.target
+    data = ce.data
 
     # Factual:
-    img = CounterfactualExplanations.factual(df.ce[1]) |> ECCCo.convert2mnist
+    img = CounterfactualExplanations.factual(ce) |> ECCCo.convert2mnist
     p1 = Plots.plot(
         img,
         axis=([], false),
@@ -85,17 +79,18 @@ function plot_random_eccco(outcome::ExperimentOutcome; generator="ECCCo-Δ", img
     )
     plts = [p1]
 
-    # Counterfactuals:
-    for (i, model) in enumerate(models)
-        img = CounterfactualExplanations.counterfactual(df.ce[i]) |> ECCCo.convert2mnist
+    for (model_name, M) in models
+        ce = generate_counterfactual(x, target, data, M, gen; initialization=:identity, converge_when=:generator_conditions)
+        img = CounterfactualExplanations.counterfactual(ce) |> ECCCo.convert2mnist
         p = Plots.plot(
             img,
             axis=([], false),
             size=(img_height, img_height),
-            title="$model",
+            title="$model_name",
         )
         push!(plts, p)
     end
+    n_models = length(models)
 
     plt = Plots.plot(
         plts...,
@@ -104,5 +99,5 @@ function plot_random_eccco(outcome::ExperimentOutcome; generator="ECCCo-Δ", img
     )
     display(plt)
 
-    return plt, df.target[1], seed
+    return plt, target, seed
 end
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