cleanup

Author: BatyLeo

src/PortfolioOptimization/PortfolioOptimization.jl

@@ -120,7 +120,7 @@ Initialize a linear model for `bench` using `Flux`.
 """
 function Utils.generate_statistical_model(bench::PortfolioOptimizationBenchmark)
     (; p, d) = bench
-    return Chain(Dense(p, d))
+    return Dense(p, d)
 end
 
 export PortfolioOptimizationBenchmark

test/portfolio_optimization.jl

@@ -1,36 +1,9 @@
 @testitem "Portfolio Optimization" begin
     using DecisionFocusedLearningBenchmarks
-    using InferOpt
-    using Flux
-    using Zygote
 
     b = PortfolioOptimizationBenchmark()
 
     dataset = generate_dataset(b, 100)
     model = generate_statistical_model(b)
     maximizer = generate_maximizer(b)
-
-    # train_dataset, test_dataset = dataset[1:50], dataset[50:100]
-    # X_train = train_dataset.features
-    # Y_train = train_dataset.solutions
-
-    # perturbed_maximizer = PerturbedAdditive(maximizer; ε=0.1, nb_samples=1)
-    # loss = FenchelYoungLoss(perturbed_maximizer)
-
-    # starting_gap = compute_gap(b, test_dataset, model, maximizer)
-
-    # opt_state = Flux.setup(Adam(), model)
-    # loss_history = Float64[]
-    # for epoch in 1:50
-    #     val, grads = Flux.withgradient(model) do m
-    #         sum(loss(m(x), y) for (x, y) in zip(X_train, Y_train)) / length(train_dataset)
-    #     end
-    #     Flux.update!(opt_state, model, grads[1])
-    #     push!(loss_history, val)
-    # end
-
-    # final_gap = compute_gap(b, test_dataset, model, maximizer)
-
-    # @test loss_history[end] < loss_history[1]
-    # @test final_gap < starting_gap / 10
 end

test/subset_selection.jl

@@ -1,9 +1,5 @@
 @testitem "Subset selection" begin
     using DecisionFocusedLearningBenchmarks
-    using InferOpt
-    using Flux
-    using UnicodePlots
-    using Zygote
 
     n = 25
     k = 5