Add more tests for better coverage

src/FixedSizeShortestPath/FixedSizeShortestPath.jl

@@ -125,13 +125,13 @@ function Utils.generate_dataset(
         [rand(rng, Uniform{type}(1 - ν, 1 + ν), E) for _ in 1:dataset_size]
     end
     costs = [
-        (1 .+ (3 .+ B * zᵢ ./ type(sqrt(p))) .^ deg) .* ξᵢ for (ξᵢ, zᵢ) in zip(ξ, features)
+        -(1 .+ (3 .+ B * zᵢ ./ type(sqrt(p))) .^ deg) .* ξᵢ for (ξᵢ, zᵢ) in zip(ξ, features)
     ]
 
     shortest_path_maximizer = Utils.generate_maximizer(bench)
 
     # Label solutions
-    solutions = shortest_path_maximizer.(.-costs)
+    solutions = shortest_path_maximizer.(costs)
     return [DataSample(; x=x, θ=θ, y=y) for (x, θ, y) in zip(features, costs, solutions)]
 end
 
@@ -145,23 +145,6 @@ function Utils.generate_statistical_model(bench::FixedSizeShortestPathBenchmark)
     return Chain(Dense(p, ne(graph)))
 end
 
-function objective_value(::FixedSizeShortestPathBenchmark, θ, y)
-    return dot(θ, y)
-end
-
-function Utils.compute_gap(
-    bench::FixedSizeShortestPathBenchmark, model, features, costs, solutions, maximizer
-)
-    res = 0.0
-    for (x, ȳ, θ̄) in zip(features, solutions, costs)
-        θ = model(x)
-        y = maximizer(θ)
-        val = objective_value(bench, θ̄, ȳ)
-        res += (objective_value(bench, θ̄, y) - val) / val
-    end
-    return res / length(features)
-end
-
 export FixedSizeShortestPathBenchmark
 
 end

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/fixed_size_shortest_path.jl

@@ -1,44 +1,35 @@
-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"))
-
+@testitem "FixedSizeShortestPath" begin
+    using DecisionFocusedLearningBenchmarks.FixedSizeShortestPath
+    using Graphs
+
+    p = 5
+    grid_size = (5, 5)
+    A = (grid_size[1] - 1) * grid_size[2] + grid_size[1] * (grid_size[2] - 1)
+    b = FixedSizeShortestPathBenchmark(; p=p, grid_size=grid_size)
+
+    @test nv(b.graph) == grid_size[1] * grid_size[2]
+    @test ne(b.graph) == A
+
+    dataset = generate_dataset(b, 50)
+    model = generate_statistical_model(b)
+    maximizer = generate_maximizer(b)
+
+    gap = compute_gap(b, dataset, model, maximizer)
+    @test gap >= 0
+
+    for sample in dataset
+        x = sample.x
+        θ_true = sample.θ
+        y_true = sample.y
+        @test all(θ_true .< 0)
+        @test size(x) == (p,)
+        @test length(θ_true) == A
+        @test length(y_true) == A
+        @test isnothing(sample.instance)
+        @test all(y_true .== maximizer(θ_true))
+        θ = model(x)
+        @test length(θ) == length(θ_true)
+        y = maximizer(θ)
+        @test length(y) == length(y_true)
+    end
 end

test/portfolio_optimization.jl

@@ -1,36 +1,29 @@
 @testitem "Portfolio Optimization" begin
     using DecisionFocusedLearningBenchmarks
-    using InferOpt
-    using Flux
-    using Zygote
 
-    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)
 
-    # 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
+    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

test/subset_selection.jl

@@ -1,38 +1,37 @@
 @testitem "Subset selection" begin
     using DecisionFocusedLearningBenchmarks
-    using InferOpt
-    using Flux
-    using UnicodePlots
-    using Zygote
 
-    b = SubsetSelectionBenchmark()
+    n = 25
+    k = 5
 
-    dataset = generate_dataset(b, 500)
-    model = generate_statistical_model(b)
-    maximizer = generate_maximizer(b)
-
-    # train_dataset, test_dataset = dataset[1:450], dataset[451:500]
-    # X_train = train_dataset.features
-    # Y_train = train_dataset.solutions
-
-    # perturbed_maximizer = PerturbedAdditive(maximizer; ε=1.0, nb_samples=100)
-    # loss = FenchelYoungLoss(perturbed_maximizer)
+    b = SubsetSelectionBenchmark(; n=n, k=k)
 
-    # starting_gap = compute_gap(b, test_dataset, model, maximizer)
+    io = IOBuffer()
+    show(io, b)
+    @test String(take!(io)) == "SubsetSelectionBenchmark(n=25, k=5)"
 
-    # opt_state = Flux.setup(Adam(0.1), 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)
+    dataset = generate_dataset(b, 50)
+    model = generate_statistical_model(b)
+    maximizer = generate_maximizer(b)
 
-    # lineplot(loss_history)
-    # @test loss_history[end] < loss_history[1]
-    # @test final_gap < starting_gap / 10
+    for (i, sample) in enumerate(dataset)
+        x = sample.x
+        θ_true = sample.θ
+        y_true = sample.y
+        @test size(x) == (n,)
+        @test length(θ_true) == n
+        @test length(y_true) == n
+        @test isnothing(sample.instance)
+        @test all(y_true .== maximizer(θ_true))
+
+        # Features and true weights should be equal
+        @test all(θ_true .== x)
+
+        θ = model(x)
+        @test length(θ) == n
+
+        y = maximizer(θ)
+        @test length(y) == n
+        @test sum(y) == k
+    end
 end

test/warcraft.jl

@@ -1,6 +1,7 @@
 @testitem "Warcraft" begin
     using DecisionFocusedLearningBenchmarks
     using DecisionFocusedLearningBenchmarks.Utils: objective_value
+    using Plots
 
     b = WarcraftBenchmark()
 
@@ -12,6 +13,11 @@
     bellman_maximizer = generate_maximizer(b; dijkstra=false)
     dijkstra_maximizer = generate_maximizer(b; dijkstra=true)
 
+    figure = plot_data(b, dataset[1])
+    @test figure isa Plots.Plot
+    gap = compute_gap(b, dataset, model, dijkstra_maximizer)
+    @test gap >= 0
+
     for (i, sample) in enumerate(dataset)
         x = sample.x
         θ_true = sample.θ