uh

test/Project.toml

@@ -1,3 +1,8 @@
 [deps]
 CompatHelperLocal = "5224ae11-6099-4aaa-941d-3aab004bd678"
+CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/runtests.jl

@@ -2,8 +2,41 @@ import CompatHelperLocal as CHL
 CHL.@check()
 
 using AlgorithmicRecourseDynamics
+using AlgorithmicRecourseDynamics.Data
+using AlgorithmicRecourseDynamics.Experiments
+using AlgorithmicRecourseDynamics.Models
+using AlgorithmicRecourseDynamics: run!
+using CounterfactualExplanations
+using Flux
+using MLJBase
+using Plots
+using Random
 using Test
 
 @testset "AlgorithmicRecourseDynamics.jl" begin
-    # Write your tests here.
+
+    N = 1000
+    xmax = 2
+    X, ys = make_blobs(
+        N, 2;
+        centers=2, as_table=false, center_box=(-xmax => xmax), cluster_std=0.1
+    )
+    ys .= ys .== 2
+    X = X'
+    counterfactual_data = CounterfactualData(X, ys')
+
+    n_epochs = 100
+    model = Chain(Dense(2, 1))
+    mod = FluxModel(model)
+    generator = GenericGenerator()
+
+    data_train, data_test = Data.train_test_split(counterfactual_data)
+    Models.train(mod, data_train; n_epochs=n_epochs)
+
+    models = Dict(:mymodel => mod)
+    generators = Dict(:wachter => generator)
+    experiment = set_up_experiment(data_train, data_test, models, generators)
+
+    run!(experiment)
+
 end