DataSample interface redesign

docs/src/tutorials/warcraft_tutorial.jl

@@ -29,8 +29,8 @@ x = sample.x
 θ_true = sample.θ
 # `y` correspond to the optimal shortest path, encoded as a binary matrix:
 y_true = sample.y
-# `info` is not used in this benchmark, therefore set to nothing:
-isnothing(sample.info)
+# `context` is not used in this benchmark (no solver kwargs needed), so it is empty:
+isempty(sample.context)
 
 # For some benchmarks, we provide the following plotting method [`plot_data`](@ref) to visualize the data:
 plot_data(b, sample)

src/DynamicVehicleScheduling/anticipative_solver.jl

@@ -224,7 +224,7 @@ function anticipative_solver(
             compute_features(state, env.instance)
         end
 
-        return DataSample(; y=y_true, x, state, reward)
+        return DataSample(; y=y_true, x, instance=state, extra=(; reward))
     end
 
     return obj, dataset

src/DynamicVehicleScheduling/plot.jl

@@ -208,8 +208,8 @@ The returned dictionary contains:
 This lets plotting code build figures without depending on plotting internals.
 """
 function build_plot_data(data_samples::Vector{<:DataSample})
-    state_data = [build_state_data(sample.info.state) for sample in data_samples]
-    rewards = [sample.info.reward for sample in data_samples]
+    state_data = [build_state_data(sample.instance) for sample in data_samples]
+    rewards = [sample.reward for sample in data_samples]
     routess = [sample.y for sample in data_samples]
     return [
         (; state..., reward, routes) for
@@ -273,8 +273,8 @@ function plot_epochs(
     # Create subplots
     plots = map(1:n_epochs) do i
         sample = data_samples[i]
-        state = sample.info.state
-        reward = sample.info.reward
+        state = sample.instance
+        reward = sample.reward
 
         common_kwargs = Dict(
             :xlims => xlims,
@@ -351,7 +351,7 @@ function animate_epochs(
     kwargs...,
 )
     pd = build_plot_data(data_samples)
-    epoch_costs = [-sample.info.reward for sample in data_samples]
+    epoch_costs = [-sample.reward for sample in data_samples]
 
     # Calculate global xlims and ylims from all states
     x_min = minimum(min(data.x_depot, minimum(data.x_customers)) for data in pd)
@@ -393,7 +393,7 @@ function animate_epochs(
     anim = @animate for frame_idx in 1:total_frames
         epoch_idx, frame_type = frame_plan[frame_idx]
         sample = data_samples[epoch_idx]
-        state = sample.info.state
+        state = sample.instance
 
         if frame_type == :routes
             fig = plot_routes(

src/Utils/data_sample.jl

@@ -3,14 +3,20 @@ $TYPEDEF
 
 Data sample data structure.
 Its main purpose is to store datasets generated by the benchmarks.
-It has 3 main fields: features `x`, cost parameters `θ` and solution `y`.
-Additionally, it has an `info` field to store any additional information as a `NamedTuple`, usually the instance, but can be used for anything else.
+It has 3 main (optional) fields: features `x`, cost parameters `θ`, and solution `y`.
+Additionally, it has a `context` field (solver kwargs, spread into the maximizer as
+`maximizer(θ; sample.context...)`) and an `extra` field (non-solver data, never passed
+to the maximizer).
+
+The separation prevents silent breakage from accidentally passing non-solver data
+(e.g. a scenario, reward, or step counter) as unexpected kwargs to the maximizer.
 
 # Fields
 $TYPEDFIELDS
 """
 struct DataSample{
-    I<:NamedTuple,
+    CTX<:NamedTuple,
+    EX<:NamedTuple,
     F<:Union{AbstractArray,Nothing},
     S<:Union{AbstractArray,Nothing},
     C<:Union{AbstractArray,Nothing},
@@ -21,53 +27,81 @@ struct DataSample{
     θ::C
     "output solution (optional)"
     y::S
-    "additional information, usually the instance (optional)"
-    info::I
+    "context information as solver kwargs, e.g. instance, graph, etc."
+    context::CTX
+    "additional data, never passed to the maximizer, e.g. scenario, objective value, reward,
+    step count, etc. Can be used for any purpose by the user, such as plotting utilities."
+    extra::EX
 end
 
 """
 $TYPEDSIGNATURES
 
 Constructor for `DataSample` with keyword arguments.
 
-Additional keyword arguments beyond `x`, `θ`, and `y` are stored in the `info` field
-and can be accessed directly (e.g., `data.instance` instead of `data.info.instance`).
+All keyword arguments beyond `x`, `θ`, `y`, and `extra` are collected into the `context`
+field (solver kwargs). The `extra` keyword accepts a `NamedTuple` of non-solver data.
+
+Fields in `context` and `extra` must be disjoint. An error is thrown if they overlap.
+Both can be accessed directly via property forwarding.
 
 # Examples
 ```julia
+# Instance goes in context
 d = DataSample(x=[1,2,3], θ=[4,5,6], y=[7,8,9], instance="my_instance")
-d.instance  # "my_instance"
+d.instance  # "my_instance" (from context)
+
+# Scenario goes in extra
+d = DataSample(x=x, y=y, instance=inst, extra=(; scenario=ξ))
+d.scenario  # ξ (from extra)
+
+# State goes in context, reward in extra
+d = DataSample(x=x, y=y, instance=state, extra=(; reward=-1.5))
+d.instance  # state (from context)
+d.reward    # -1.5 (from extra)
 ```
 """
-function DataSample(; x=nothing, θ=nothing, y=nothing, kwargs...)
-    info = (; kwargs...)
-    return DataSample(x, θ, y, info)
+function DataSample(; x=nothing, θ=nothing, y=nothing, extra=NamedTuple(), kwargs...)
+    context = (; kwargs...)
+    overlap = intersect(keys(context), keys(extra))
+    if !isempty(overlap)
+        error("Keys $(collect(overlap)) appear in both context and extra of DataSample")
+    end
+    return DataSample(x, θ, y, context, extra)
 end
 
 """
 $TYPEDSIGNATURES
 
 Extended property access for `DataSample`.
 
-Allows accessing `info` fields directly as properties (e.g., `d.instance` instead of `d.info.instance`).
+Allows accessing `context` and `extra` fields directly as properties.
+`context` is searched first; if the key is not found there, `extra` is searched.
 """
 function Base.getproperty(d::DataSample, name::Symbol)
-    if name in (:x, :θ, :y, :info)
+    if name in (:x, :θ, :y, :context, :extra)
         return getfield(d, name)
     else
-        return getproperty(getfield(d, :info), name)
+        ctx = getfield(d, :context)
+        if haskey(ctx, name)
+            return getproperty(ctx, name)
+        end
+        return getproperty(getfield(d, :extra), name)
     end
 end
 
 """
 $TYPEDSIGNATURES
 
-Return all property names of a `DataSample`, including both struct fields and `info` fields.
+Return all property names of a `DataSample`, including both struct fields and forwarded
+fields from `context` and `extra`.
 
-This enables tab completion for all available properties, including those stored in `info`.
+This enables tab completion for all available properties.
 """
 function Base.propertynames(d::DataSample, private::Bool=false)
-    return (fieldnames(DataSample)..., propertynames(getfield(d, :info), private)...)
+    ctx_names = propertynames(getfield(d, :context), private)
+    extra_names = propertynames(getfield(d, :extra), private)
+    return (fieldnames(DataSample)..., ctx_names..., extra_names...)
 end
 
 """
@@ -92,7 +126,11 @@ function Base.show(io::IO, d::DataSample)
         y_str = sprint(show, d.y; context=io_limited)
         push!(fields, "y_true=$y_str")
     end
-    for (key, value) in pairs(d.info)
+    for (key, value) in pairs(d.context)
+        value_str = sprint(show, value; context=io_limited)
+        push!(fields, "$key=$value_str")
+    end
+    for (key, value) in pairs(d.extra)
         value_str = sprint(show, value; context=io_limited)
         push!(fields, "$key=$value_str")
     end
@@ -116,8 +154,8 @@ Transform the features in the dataset.
 """
 function StatsBase.transform(t, dataset::AbstractVector{<:DataSample})
     return map(dataset) do d
-        (; info, x, θ, y) = d
-        DataSample(StatsBase.transform(t, x), θ, y, info)
+        (; context, extra, x, θ, y) = d
+        DataSample(StatsBase.transform(t, x), θ, y, context, extra)
     end
 end
 
@@ -139,8 +177,8 @@ Reconstruct the features in the dataset.
 """
 function StatsBase.reconstruct(t, dataset::AbstractVector{<:DataSample})
     return map(dataset) do d
-        (; info, x, θ, y) = d
-        DataSample(StatsBase.reconstruct(t, x), θ, y, info)
+        (; context, extra, x, θ, y) = d
+        DataSample(StatsBase.reconstruct(t, x), θ, y, context, extra)
     end
 end
 

src/Utils/interface.jl

@@ -111,8 +111,8 @@ $TYPEDSIGNATURES
 Compute the objective value of given solution `y`.
 """
 function objective_value(
-    bench::AbstractBenchmark, sample::DataSample{I,F,S,C}, y::AbstractArray
-) where {I,F,S,C<:AbstractArray}
+    bench::AbstractBenchmark, sample::DataSample{CTX,EX,F,S,C}, y::AbstractArray
+) where {CTX,EX,F,S,C<:AbstractArray}
     return objective_value(bench, sample.θ, y)
 end
 
@@ -122,8 +122,8 @@ $TYPEDSIGNATURES
 Compute the objective value of the target in the sample (needs to exist).
 """
 function objective_value(
-    bench::AbstractBenchmark, sample::DataSample{I,F,S,C}
-) where {I,F,S<:AbstractArray,C}
+    bench::AbstractBenchmark, sample::DataSample{CTX,EX,F,S,C}
+) where {CTX,EX,F,S<:AbstractArray,C}
     return objective_value(bench, sample, sample.y)
 end
 
@@ -155,7 +155,7 @@ function compute_gap(
             target_obj = objective_value(bench, sample)
             x = sample.x
             θ = statistical_model(x)
-            y = maximizer(θ; sample.info...)
+            y = maximizer(θ; sample.context...)
             obj = objective_value(bench, sample, y)
             Δ = check ? obj - target_obj : target_obj - obj
             return Δ / abs(target_obj)

src/Utils/policy.jl

@@ -44,7 +44,7 @@ function evaluate_policy!(
         features, state = observe(env)
         state_copy = deepcopy(state)  # To avoid mutation issues
         reward = step!(env, y)
-        sample = DataSample(; x=features, y=y, state=state_copy, reward=reward)
+        sample = DataSample(; x=features, y=y, instance=state_copy, extra=(; reward))
         if isempty(labeled_dataset)
             labeled_dataset = typeof(sample)[sample]
         else

test/argmax.jl

@@ -24,7 +24,7 @@
         @test size(x) == (nb_features, instance_dim)
         @test length(θ_true) == instance_dim
         @test length(y_true) == instance_dim
-        @test length(sample.info) == 0
+        @test isempty(sample.context)
         @test all(y_true .== maximizer(θ_true))
 
         θ = model(x)

test/dynamic_assortment.jl

@@ -321,7 +321,7 @@ end
 
     # Test integration with sample data
     sample = generate_sample(b, MersenneTwister(42))
-    @test hasfield(typeof(sample), :info)
+    @test hasfield(typeof(sample), :context)
 
     dataset = generate_dataset(b, 3; seed=42)
     environments = generate_environments(b, dataset)

test/dynamic_vsp.jl

@@ -50,7 +50,7 @@
     anticipative_value, solution = generate_anticipative_solution(b, env; reset_env=true)
     reset!(env; reset_rng=true)
     cost = sum(step!(env, sample.y) for sample in solution)
-    cost2 = sum(sample.info.reward for sample in solution)
+    cost2 = sum(sample.reward for sample in solution)
     @test isapprox(cost, anticipative_value; atol=1e-5)
     @test isapprox(cost, cost2; atol=1e-5)
 end

test/dynamic_vsp_plots.jl

@@ -22,7 +22,7 @@
     policies = generate_policies(b)
     lazy = policies[1]
     _, d = evaluate_policy!(lazy, env)
-    fig3 = DVSP.plot_routes(d[1].info.state, d[1].y)
+    fig3 = DVSP.plot_routes(d[1].instance, d[1].y)
     @test fig3 isa Plots.Plot
 
     # Test animation

test/fixed_size_shortest_path.jl

@@ -25,7 +25,7 @@
         @test size(x) == (p,)
         @test length(θ_true) == A
         @test length(y_true) == A
-        @test length(sample.info) == 0
+        @test isempty(sample.context)
         @test all(y_true .== maximizer(θ_true))
         θ = model(x)
         @test length(θ) == length(θ_true)

test/maintenance.jl

@@ -198,7 +198,7 @@ end
 
     # Test integration with sample data
     sample = generate_sample(b, MersenneTwister(42))
-    @test hasfield(typeof(sample), :info)
+    @test hasfield(typeof(sample), :context)
 
     dataset = generate_dataset(b, 3; seed=42)
     environments = generate_environments(b, dataset)

test/portfolio_optimization.jl

@@ -16,7 +16,7 @@
         @test size(x) == (p,)
         @test length(θ_true) == d
         @test length(y_true) == d
-        @test length(sample.info) == 0
+        @test isempty(sample.context)
         @test all(y_true .== maximizer(θ_true))
 
         θ = model(x)

test/ranking.jl

@@ -21,7 +21,7 @@
         @test size(x) == (nb_features, instance_dim)
         @test length(θ_true) == instance_dim
         @test length(y_true) == instance_dim
-        @test length(sample.info) == 0
+        @test isempty(sample.context)
         @test all(y_true .== maximizer(θ_true))
 
         θ = model(x)

test/subset_selection.jl

@@ -23,7 +23,7 @@
         @test size(x) == (n,)
         @test length(θ_true) == n
         @test length(y_true) == n
-        @test length(sample.info) == 0
+        @test isempty(sample.context)
         @test all(y_true .== maximizer(θ_true))
 
         # Features and true weights should be equal

test/utils.jl

@@ -58,7 +58,7 @@ end
     @test occursin("y_true", s)
     @test occursin("instance=\"this is an instance\"", s)
 
-    @test propertynames(sample) == (:x, :θ, :y, :info, :instance)
+    @test propertynames(sample) == (:x, :θ, :y, :context, :extra, :instance)
 
     # Create a dataset for testing
     N = 5
@@ -81,7 +81,7 @@ end
     for i in 1:N
         @test dataset_zt[i].θ == dataset[i].θ
         @test dataset_zt[i].y == dataset[i].y
-        @test dataset_zt[i].info == dataset[i].info
+        @test dataset_zt[i].context == dataset[i].context
     end
 
     # Check that features are actually transformed
@@ -97,7 +97,7 @@ end
     for i in 1:N
         @test dataset_copy[i].θ == dataset[i].θ
         @test dataset_copy[i].y == dataset[i].y
-        @test dataset_copy[i].info == dataset[i].info
+        @test dataset_copy[i].context == dataset[i].context
     end
 
     # Test reconstruct (non-mutating)
@@ -109,7 +109,7 @@ end
         @test dataset_reconstructed[i].x ≈ dataset[i].x atol = 1e-10
         @test dataset_reconstructed[i].θ == dataset[i].θ
         @test dataset_reconstructed[i].y == dataset[i].y
-        @test dataset_reconstructed[i].info == dataset[i].info
+        @test dataset_reconstructed[i].context == dataset[i].context
     end
 
     # Test reconstruct! (mutating)

test/warcraft.jl

@@ -24,7 +24,7 @@
         y_true = sample.y
         @test size(x) == (96, 96, 3, 1)
         @test all(θ_true .<= 0)
-        @test length(sample.info) == 0
+        @test isempty(sample.context)
 
         θ = model(x)
         @test size(θ) == size(θ_true)