portfolio optimization tests

Author: BatyLeo

test/fixed_size_shortest_path.jl

@@ -2,43 +2,11 @@ module FixedSizeShortestPathTest
 
 using DecisionFocusedLearningBenchmarks.FixedSizeShortestPath
 
-# using Flux
-# using InferOpt
-# using ProgressMeter
-# using UnicodePlots
-# using Zygote
-
 bench = FixedSizeShortestPathBenchmark()
 
 (; features, costs, solutions) = generate_dataset(bench)
 
 model = generate_statistical_model(bench)
 maximizer = generate_maximizer(bench)
 
-# perturbed = PerturbedAdditive(maximizer; nb_samples=10, ε=0.1)
-# fyl = FenchelYoungLoss(perturbed)
-
-# opt_state = Flux.setup(Adam(), model)
-# loss_history = Float64[]
-# gap_history = Float64[]
-# E = 100
-# @showprogress for epoch in 1:E
-#     loss = 0.0
-#     for (x, y) in zip(features, solutions)
-#         val, grads = Flux.withgradient(model) do m
-#             θ = m(x)
-#             fyl(θ, y)
-#         end
-#         loss += val
-#         Flux.update!(opt_state, model, grads[1])
-#     end
-#     push!(loss_history, loss ./ E)
-#     push!(
-#         gap_history, compute_gap(bench, model, features, costs, solutions, maximizer) .* 100
-#     )
-# end
-
-# println(lineplot(loss_history; title="Loss"))
-# println(lineplot(gap_history; title="Gap"))
-
 end

test/portfolio_optimization.jl

@@ -1,9 +1,29 @@
 @testitem "Portfolio Optimization" begin
     using DecisionFocusedLearningBenchmarks
 
-    b = PortfolioOptimizationBenchmark()
+    d = 50
+    p = 5
+    b = PortfolioOptimizationBenchmark(; d=d, p=p)
 
-    dataset = generate_dataset(b, 100)
+    dataset = generate_dataset(b, 50)
     model = generate_statistical_model(b)
     maximizer = generate_maximizer(b)
+
+    for sample in dataset
+        x = sample.x
+        θ_true = sample.θ
+        y_true = sample.y
+        @test size(x) == (p,)
+        @test length(θ_true) == d
+        @test length(y_true) == d
+        @test isnothing(sample.instance)
+        @test all(y_true .== maximizer(θ_true))
+
+        θ = model(x)
+        @test length(θ) == d
+
+        y = maximizer(θ)
+        @test length(y) == d
+        @test sum(y) <= 1
+    end
 end