Implement argmax and ranking benchmarks

.gitignore

@@ -2,6 +2,7 @@
 /docs/src/index.md
 data
 scripts
+.DS_Store
 
 # Files generated by invoking Julia with --code-coverage
 *.jl.cov

docs/src/api/argmax.md

@@ -0,0 +1,15 @@
+# Argmax
+
+## Public
+
+```@autodocs
+Modules = [DecisionFocusedLearningBenchmarks.Argmax]
+Private = false
+```
+
+## Private
+
+```@autodocs
+Modules = [DecisionFocusedLearningBenchmarks.Argmax]
+Public = false
+```

docs/src/api/ranking.md

@@ -0,0 +1,15 @@
+# Ranking
+
+## Public
+
+```@autodocs
+Modules = [DecisionFocusedLearningBenchmarks.Ranking]
+Private = false
+```
+
+## Private
+
+```@autodocs
+Modules = [DecisionFocusedLearningBenchmarks.Ranking]
+Public = false
+```

src/Argmax/Argmax.jl

@@ -0,0 +1,86 @@
+module Argmax
+
+using ..Utils
+using DocStringExtensions: TYPEDEF, TYPEDFIELDS, TYPEDSIGNATURES
+using Flux: Chain, Dense
+using Random
+
+"""
+$TYPEDEF
+
+Benchmark problem with an argmax as the CO algorithm.
+
+# Fields
+$TYPEDFIELDS
+"""
+struct ArgmaxBenchmark <: AbstractBenchmark
+    "iinstances dimension, total number of classes"
+    instance_dim::Int
+    "number of features"
+    nb_features::Int
+end
+
+function Base.show(io::IO, bench::ArgmaxBenchmark)
+    (; instance_dim, nb_features) = bench
+    return print(
+        io, "ArgmaxBenchmark(instance_dim=$instance_dim, nb_features=$nb_features)"
+    )
+end
+
+function ArgmaxBenchmark(; instance_dim::Int=10, nb_features::Int=5)
+    return ArgmaxBenchmark(instance_dim, nb_features)
+end
+
+"""
+$TYPEDSIGNATURES
+
+One-hot encoding of the argmax function.
+"""
+function one_hot_argmax(z::AbstractVector{R}; kwargs...) where {R<:Real}
+    e = zeros(R, length(z))
+    e[argmax(z)] = one(R)
+    return e
+end
+
+"""
+$TYPEDSIGNATURES
+
+Return a top k maximizer.
+"""
+function Utils.generate_maximizer(bench::ArgmaxBenchmark)
+    return one_hot_argmax
+end
+
+"""
+$TYPEDSIGNATURES
+
+Generate a dataset of labeled instances for the subset selection problem.
+The mapping between features and cost is identity.
+"""
+function Utils.generate_dataset(bench::ArgmaxBenchmark, dataset_size::Int=10; seed::Int=0)
+    (; instance_dim, nb_features) = bench
+    rng = MersenneTwister(seed)
+    features = [randn(rng, Float32, nb_features, instance_dim) for _ in 1:dataset_size]
+    mapping = Chain(Dense(nb_features => 1; bias=false), vec)
+    costs = mapping.(features)
+    solutions = one_hot_argmax.(costs)
+    return [
+        DataSample(; x, θ_true, y_true) for
+        (x, θ_true, y_true) in zip(features, costs, solutions)
+    ]
+end
+
+"""
+$TYPEDSIGNATURES
+
+Initialize a linear model for `bench` using `Flux`.
+"""
+function Utils.generate_statistical_model(bench::ArgmaxBenchmark; seed=0)
+    Random.seed!(seed)
+    (; nb_features) = bench
+    return Chain(Dense(nb_features => 1; bias=false), vec)
+end
+
+export ArgmaxBenchmark
+
+end

src/DecisionFocusedLearningBenchmarks.jl

@@ -19,16 +19,20 @@ end
 
 include("Utils/Utils.jl")
 
+include("Argmax/Argmax.jl")
+include("Ranking/Ranking.jl")
+include("SubsetSelection/SubsetSelection.jl")
 include("Warcraft/Warcraft.jl")
 include("FixedSizeShortestPath/FixedSizeShortestPath.jl")
 include("PortfolioOptimization/PortfolioOptimization.jl")
-include("SubsetSelection/SubsetSelection.jl")
 
 using .Utils
+using .Argmax
+using .Ranking
+using .SubsetSelection
 using .Warcraft
 using .FixedSizeShortestPath
 using .PortfolioOptimization
-using .SubsetSelection
 
 # Interface
 export AbstractBenchmark, DataSample
@@ -39,9 +43,11 @@ export plot_data
 export compute_gap
 
 # Export all benchmarks
+export ArgmaxBenchmark
+export RankingBenchmark
+export SubsetSelectionBenchmark
 export WarcraftBenchmark
 export FixedSizeShortestPathBenchmark
 export PortfolioOptimizationBenchmark
-export SubsetSelectionBenchmark
 
 end # module DecisionFocusedLearningBenchmarks

src/Ranking/Ranking.jl

@@ -0,0 +1,84 @@
+module Ranking
+
+using ..Utils
+using DocStringExtensions: TYPEDEF, TYPEDFIELDS, TYPEDSIGNATURES
+using Flux: Chain, Dense
+using Random
+
+"""
+$TYPEDEF
+
+Benchmark problem with an argmax as the CO algorithm.
+
+# Fields
+$TYPEDFIELDS
+"""
+struct RankingBenchmark <: AbstractBenchmark
+    "iinstances dimension, total number of classes"
+    instance_dim::Int
+    "number of features"
+    nb_features::Int
+end
+
+function Base.show(io::IO, bench::RankingBenchmark)
+    (; instance_dim, nb_features) = bench
+    return print(
+        io, "RankingBenchmark(instance_dim=$instance_dim, nb_features=$nb_features)"
+    )
+end
+
+function RankingBenchmark(; instance_dim::Int=10, nb_features::Int=5)
+    return RankingBenchmark(instance_dim, nb_features)
+end
+
+"""
+$TYPEDSIGNATURES
+
+Compute the vector `r` such that `rᵢ` is the rank of `θᵢ` in `θ`.
+"""
+function ranking(θ::AbstractVector; rev::Bool=false, kwargs...)
+    return invperm(sortperm(θ; rev=rev))
+end
+
+"""
+$TYPEDSIGNATURES
+
+Return a top k maximizer.
+"""
+function Utils.generate_maximizer(bench::RankingBenchmark)
+    return ranking
+end
+
+"""
+$TYPEDSIGNATURES
+
+Generate a dataset of labeled instances for the subset selection problem.
+The mapping between features and cost is identity.
+"""
+function Utils.generate_dataset(bench::RankingBenchmark, dataset_size::Int=10; seed::Int=0)
+    (; instance_dim, nb_features) = bench
+    rng = MersenneTwister(seed)
+    features = [randn(rng, Float32, nb_features, instance_dim) for _ in 1:dataset_size]
+    mapping = Chain(Dense(nb_features => 1; bias=false), vec)
+    costs = mapping.(features)
+    solutions = ranking.(costs)
+    return [
+        DataSample(; x, θ_true, y_true) for
+        (x, θ_true, y_true) in zip(features, costs, solutions)
+    ]
+end
+
+"""
+$TYPEDSIGNATURES
+
+Initialize a linear model for `bench` using `Flux`.
+"""
+function Utils.generate_statistical_model(bench::RankingBenchmark; seed=0)
+    Random.seed!(seed)
+    (; nb_features) = bench
+    return Chain(Dense(nb_features => 1; bias=false), vec)
+end
+
+export RankingBenchmark
+
+end

test/argmax.jl

@@ -0,0 +1,31 @@
+@testitem "Argmax" begin
+    using DecisionFocusedLearningBenchmarks
+
+    instance_dim = 10
+    nb_features = 5
+
+    b = ArgmaxBenchmark(; instance_dim=instance_dim, nb_features=nb_features)
+
+    io = IOBuffer()
+    show(io, b)
+    @test String(take!(io)) == "ArgmaxBenchmark(instance_dim=10, nb_features=5)"
+
+    dataset = generate_dataset(b, 50)
+    model = generate_statistical_model(b)
+    maximizer = generate_maximizer(b)
+
+    for (i, sample) in enumerate(dataset)
+        (; x, θ_true, y_true) = sample
+        @test size(x) == (nb_features, instance_dim)
+        @test length(θ_true) == instance_dim
+        @test length(y_true) == instance_dim
+        @test isnothing(sample.instance)
+        @test all(y_true .== maximizer(θ_true))
+
+        θ = model(x)
+        @test length(θ) == instance_dim
+
+        y = maximizer(θ)
+        @test length(y) == instance_dim
+    end
+end

test/ranking.jl

@@ -0,0 +1,31 @@
+@testitem "Ranking" begin
+    using DecisionFocusedLearningBenchmarks
+
+    instance_dim = 10
+    nb_features = 5
+
+    b = RankingBenchmark(; instance_dim=instance_dim, nb_features=nb_features)
+
+    io = IOBuffer()
+    show(io, b)
+    @test String(take!(io)) == "RankingBenchmark(instance_dim=10, nb_features=5)"
+
+    dataset = generate_dataset(b, 50)
+    model = generate_statistical_model(b)
+    maximizer = generate_maximizer(b)
+
+    for (i, sample) in enumerate(dataset)
+        (; x, θ_true, y_true) = sample
+        @test size(x) == (nb_features, instance_dim)
+        @test length(θ_true) == instance_dim
+        @test length(y_true) == instance_dim
+        @test isnothing(sample.instance)
+        @test all(y_true .== maximizer(θ_true))
+
+        θ = model(x)
+        @test length(θ) == instance_dim
+
+        y = maximizer(θ)
+        @test length(y) == instance_dim
+    end
+end