come on then

Author: pat-alt

Project.toml

@@ -25,7 +25,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
 CSV = "0.10"
-CounterfactualExplanations = "0.1 - 0"
+CounterfactualExplanations = "0.1 - 0.1.6"
 DataFrames = "1"
 Distances = "0.10"
 Flux = "0.13 - 0.14"

src/experiments/functions.jl

@@ -172,14 +172,15 @@ function update_experiment!(experiment::Experiment, recourse_system::RecourseSys
         indices_ = rand(1:experiment.num_counterfactuals, length(results)) # randomly draw from generated counterfactuals
         X′ = reduce(hcat, @.(selectdim(counterfactual(results), 3, indices_)))
         y′ = reduce(hcat, @.(selectdim(counterfactual_label(results), 3, indices_)))
+        y′ = [y[1] for y in y′]
 
-    # If for any counterfactuals the returned label is NaN, this is considered as invalid and the current label is not updated:
-    valid_ces = vec(.!(isnan.(y′)))
-    chosen_individuals = chosen_individuals[valid_ces]
+        # If for any counterfactuals the returned label is NaN, this is considered as invalid and the current label is not updated:
+        valid_ces = vec(.!(isnan.(y′)))
+        chosen_individuals = chosen_individuals[valid_ces]
 
-    # Update data:
-    X[:, chosen_individuals] = X′[:, valid_ces]
-    y[:, chosen_individuals] = y′[:, valid_ces]
+        # Update data:
+        X[:, chosen_individuals] = X′[:, valid_ces]
+        y[:, chosen_individuals] = y′[:, valid_ces]
 
         # Generative model:
         gen_mod = deepcopy(counterfactual_data.generative_model)

test/Project.toml

@@ -8,6 +8,6 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-CounterfactualExplanations = "0.1 - 0"
+CounterfactualExplanations = "0.1 - 0.1.6"
 Flux = "0.13 - 0.14"