Rename DataSample fields to x/theta/y/info

Author: BatyLeo

src/Argmax/Argmax.jl

@@ -76,9 +76,9 @@ function Utils.generate_sample(
 )
     (; instance_dim, nb_features, encoder) = bench
     features = randn(rng, Float32, nb_features, instance_dim)
-    costs = encoder(features)
-    noisy_solution = one_hot_argmax(costs + noise_std * randn(rng, Float32, instance_dim))
-    return DataSample(; x=features, θ_true=costs, y_true=noisy_solution)
+    θ_true = encoder(features)
+    noisy_y_true = one_hot_argmax(θ_true + noise_std * randn(rng, Float32, instance_dim))
+    return DataSample(; x=features, θ=θ_true, y=noisy_y_true)
 end
 
 """

src/Argmax2D/Argmax2D.jl

@@ -62,7 +62,7 @@ function Utils.generate_sample(bench::Argmax2DBenchmark, rng::AbstractRNG)
     θ_true ./= 2 * norm(θ_true)
     instance = build_polytope(rand(rng, polytope_vertex_range); shift=rand(rng))
     y_true = maximizer(θ_true; instance)
-    return DataSample(; x=x, θ_true=θ_true, y_true=y_true, instance=instance)
+    return DataSample(; x=x, θ=θ_true, y=y_true, info=instance)
 end
 
 """
@@ -88,11 +88,11 @@ function Utils.generate_statistical_model(
     return model
 end
 
-function Utils.plot_data(::Argmax2DBenchmark; instance, θ, kwargs...)
+function Utils.plot_data(::Argmax2DBenchmark; info, θ, kwargs...)
     pl = init_plot()
-    plot_polytope!(pl, instance)
+    plot_polytope!(pl, info)
     plot_objective!(pl, θ)
-    return plot_maximizer!(pl, θ, instance, maximizer)
+    return plot_maximizer!(pl, θ, info, maximizer)
 end
 
 """
@@ -101,13 +101,9 @@ $TYPEDSIGNATURES
 Plot the data sample for the [`Argmax2DBenchmark`](@ref).
 """
 function Utils.plot_data(
-    bench::Argmax2DBenchmark,
-    sample::DataSample;
-    instance=sample.instance,
-    θ=sample.θ_true,
-    kwargs...,
+    bench::Argmax2DBenchmark, sample::DataSample; info=sample.info, θ=sample.θ, kwargs...
 )
-    return Utils.plot_data(bench; instance, θ, kwargs...)
+    return Utils.plot_data(bench; info, θ, kwargs...)
 end
 
 export Argmax2DBenchmark

src/DynamicAssortment/DynamicAssortment.jl

@@ -83,7 +83,7 @@ Outputs a data sample containing an [`Instance`](@ref).
 function Utils.generate_sample(
     b::DynamicAssortmentBenchmark, rng::AbstractRNG=MersenneTwister(0)
 )
-    return DataSample(; instance=Instance(b, rng))
+    return DataSample(; info=Instance(b, rng))
 end
 
 """

src/DynamicVehicleScheduling/DynamicVehicleScheduling.jl

@@ -63,7 +63,7 @@ function Utils.generate_dataset(b::DynamicVehicleSchedulingBenchmark, dataset_si
     dataset_size = min(dataset_size, length(files))
     return [
         DataSample(;
-            instance=Instance(
+            info=Instance(
                 read_vsp_instance(files[i]);
                 max_requests_per_epoch,
                 Δ_dispatch,

src/DynamicVehicleScheduling/anticipative_solver.jl

@@ -92,14 +92,14 @@ function anticipative_solver(
     job_indices = 2:nb_nodes
     epoch_indices = T
 
-    @variable(model, y[i = 1:nb_nodes, j = 1:nb_nodes, t = epoch_indices]; binary=true)
+    @variable(model, y[i=1:nb_nodes, j=1:nb_nodes, t=epoch_indices]; binary=true)
 
     @objective(
         model,
         Max,
         sum(
-            -duration[i, j] * y[i, j, t] for
-            i in 1:nb_nodes, j in 1:nb_nodes, t in epoch_indices
+            -duration[i, j] * y[i, j, t] for i in 1:nb_nodes, j in 1:nb_nodes,
+            t in epoch_indices
         )
     )
 
@@ -171,12 +171,14 @@ function anticipative_solver(
         routes = epoch_routes[i]
         epoch_customers = epoch_indices[i]
 
-        y_true = VSPSolution(
-            Vector{Int}[
-                map(idx -> findfirst(==(idx), epoch_customers), route) for route in routes
-            ];
-            max_index=length(epoch_customers),
-        ).edge_matrix
+        y_true =
+            VSPSolution(
+                Vector{Int}[
+                    map(idx -> findfirst(==(idx), epoch_customers), route) for
+                    route in routes
+                ];
+                max_index=length(epoch_customers),
+            ).edge_matrix
 
         location_indices = customer_index[epoch_customers]
         new_coordinates = env.instance.static_instance.coordinate[location_indices]
@@ -200,8 +202,7 @@ function anticipative_solver(
             is_must_dispatch[2:end] .= true
         else
             is_must_dispatch[2:end] .=
-                planning_start_time .+ epoch_duration .+ @view(new_duration[1, 2:end]) .>
-                new_start_time[2:end]
+                planning_start_time .+ epoch_duration .+ @view(new_duration[1, 2:end]) .> new_start_time[2:end]
         end
         is_postponable[2:end] .= .!is_must_dispatch[2:end]
         # TODO: avoid code duplication with add_new_customers!
@@ -222,7 +223,7 @@ function anticipative_solver(
             compute_features(state, env.instance)
         end
 
-        return DataSample(; instance=(; state, reward), y_true, x)
+        return DataSample(; info=(; state, reward), y, x)
     end
 
     return obj, dataset

src/FixedSizeShortestPath/FixedSizeShortestPath.jl

@@ -121,11 +121,11 @@ function Utils.generate_sample(
     else
         rand(rng, Uniform{type}(1 - ν, 1 + ν), E)
     end
-    costs = -(1 .+ (3 .+ B * features ./ type(sqrt(p))) .^ deg) .* ξ
+    θ_true = -(1 .+ (3 .+ B * features ./ type(sqrt(p))) .^ deg) .* ξ
 
     maximizer = Utils.generate_maximizer(bench)
-    solution = maximizer(costs)
-    return DataSample(; x=features, θ_true=costs, y_true=solution)
+    y_true = maximizer(θ_true)
+    return DataSample(; x=features, θ=θ_true, y=y_true)
 end
 
 """

src/PortfolioOptimization/PortfolioOptimization.jl

@@ -94,12 +94,12 @@ function Utils.generate_sample(
     features = randn(rng, type, p)
     B = rand(rng, Bernoulli(0.5), d, p)
     c̄ = (0.05 / type(sqrt(p)) .* B * features .+ 0.1^(1 / deg)) .^ deg
-    costs = c̄ .+ L * f .+ 0.01 * ν * randn(rng, type, d)
+    θ_true = c̄ .+ L * f .+ 0.01 * ν * randn(rng, type, d)
 
     maximizer = Utils.generate_maximizer(bench)
-    solution = maximizer(costs)
+    y_true = maximizer(θ_true)
 
-    return DataSample(; x=features, θ_true=costs, y_true=solution)
+    return DataSample(; x=features, θ=θ_true, y=y_true)
 end
 
 """

src/Ranking/Ranking.jl

@@ -68,9 +68,9 @@ function Utils.generate_sample(
 )
     (; instance_dim, nb_features, encoder) = bench
     features = randn(rng, Float32, nb_features, instance_dim)
-    costs = encoder(features)
-    noisy_solution = ranking(costs .+ noise_std * randn(rng, Float32, instance_dim))
-    return DataSample(; x=features, θ_true=costs, y_true=noisy_solution)
+    θ_true = encoder(features)
+    noisy_y_true = ranking(θ_true .+ noise_std * randn(rng, Float32, instance_dim))
+    return DataSample(; x=features, θ=θ_true, y=noisy_y_true)
 end
 
 """