Adjust doc, and fix tutorial

Author: BatyLeo

docs/src/benchmark_interfaces.md

@@ -11,10 +11,10 @@ All benchmarks work with [`DataSample`](@ref) objects that encapsulate the data
 
 ```julia
 @kwdef struct DataSample{I,F,S,C}
-    x::F = nothing           # Input features  
-    θ_true::C = nothing      # True cost/utility parameters
-    y_true::S = nothing      # True optimal solution
-    instance::I = nothing    # Problem instance object/additional data
+    x::F = nothing       # Input features of the policy
+    θ::C = nothing       # Intermediate cost/utility parameters
+    y::S = nothing       # Output solution
+    info::I = nothing    # Additional data information (e.g., problem instance)
 end
 ```
 

docs/src/tutorials/warcraft_tutorial.jl

@@ -21,16 +21,16 @@ dataset = generate_dataset(b, 50);
 # Subdatasets can be created through regular slicing:
 train_dataset, test_dataset = dataset[1:45], dataset[46:50]
 
-# And getting an individual sample will return a [`DataSample`](@ref) with four fields: `x`, `instance`, `θ`, and `y`:
+# And getting an individual sample will return a [`DataSample`](@ref) with four fields: `x`, `info`, `θ`, and `y`:
 sample = test_dataset[1]
 # `x` correspond to the input features, i.e. the input image (3D array) in the Warcraft benchmark case:
 x = sample.x
-# `θ_true` correspond to the true unknown terrain weights. We use the opposite of the true weights in order to formulate the optimization problem as a maximization problem:
-θ_true = sample.θ_true
-# `y_true` correspond to the optimal shortest path, encoded as a binary matrix:
-y_true = sample.y_true
-# `instance` is not used in this benchmark, therefore set to nothing:
-isnothing(sample.instance)
+# `θ` correspond to the true unknown terrain weights. We use the opposite of the true weights in order to formulate the optimization problem as a maximization problem:
+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)
 
 # For some benchmarks, we provide the following plotting method [`plot_data`](@ref) to visualize the data:
 plot_data(b, sample)
@@ -50,7 +50,7 @@ maximizer = generate_maximizer(b; dijkstra=true)
 # In the case o fthe Warcraft benchmark, the method has an additional keyword argument to chose the algorithm to use: Dijkstra's algorithm or Bellman-Ford algorithm.
 y = maximizer(θ)
 # As we can see, currently the pipeline predicts random noise as cell weights, and therefore the maximizer returns a straight line path.
-plot_data(b, DataSample(; x, θ_true=θ, y_true=y))
+plot_data(b, DataSample(; x, θ, y))
 # We can evaluate the current pipeline performance using the optimality gap metric:
 starting_gap = compute_gap(b, test_dataset, model, maximizer)
 
@@ -70,7 +70,7 @@ opt_state = Flux.setup(Adam(1e-3), model)
 loss_history = Float64[]
 for epoch in 1:50
     val, grads = Flux.withgradient(model) do m
-        sum(loss(m(x), y_true) for (; x, y_true) in train_dataset) / length(train_dataset)
+        sum(loss(m(x), y) for (; x, y) in train_dataset) / length(train_dataset)
     end
     Flux.update!(opt_state, model, grads[1])
     push!(loss_history, val)
@@ -85,7 +85,7 @@ final_gap = compute_gap(b, test_dataset, model, maximizer)
 #
 θ = model(x)
 y = maximizer(θ)
-plot_data(b, DataSample(; x, θ_true=θ, y_true=y))
+plot_data(b, DataSample(; x, θ, y))
 
 using Test #src
 @test final_gap < starting_gap #src