Enforce info field to be a NamedTuple

.github/workflows/Test.yml

@@ -23,7 +23,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        julia-version: ['1']
+        julia-version: ['1.10', '1']
 
     steps:
       - uses: actions/checkout@v5

Project.toml

@@ -3,6 +3,9 @@ uuid = "2fbe496a-299b-4c81-bab5-c44dfc55cf20"
 authors = ["Members of JuliaDecisionFocusedLearning"]
 version = "0.3.0"
 
+[workspace]
+projects = ["docs", "test"]
+
 [deps]
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
@@ -56,31 +59,12 @@ LinearAlgebra = "1"
 Metalhead = "0.9.4"
 NPZ = "0.4"
 Plots = "1"
-Printf = "1.11.0"
+Printf = "1"
 Random = "1"
 Requires = "1.3.0"
 SCIP = "0.12"
 SimpleWeightedGraphs = "1.4"
 SparseArrays = "1"
-Statistics = "1.11.1"
+Statistics = "1"
 StatsBase = "0.34.4"
-julia = "1.6"
-
-[extras]
-Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
-Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
-JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
-JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
-ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
-UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
-
-[targets]
-test = ["Aqua", "Documenter", "Flux", "Graphs", "JET", "JuliaFormatter", "Random", "ProgressMeter", "StableRNGs", "Statistics", "Test", "TestItemRunner", "UnicodePlots", "Zygote"]
+julia = "1.10"

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

src/DecisionFocusedLearningBenchmarks.jl

@@ -70,7 +70,7 @@ export generate_sample, generate_dataset, generate_environments, generate_enviro
 export generate_scenario
 export generate_policies
 export generate_statistical_model
-export generate_maximizer, maximizer_kwargs
+export generate_maximizer
 export generate_anticipative_solution
 export is_exogenous, is_endogenous
 

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(; info=Instance(b, rng))
+    return DataSample(; instance=Instance(b, rng))
 end
 
 """

src/DynamicVehicleScheduling/DynamicVehicleScheduling.jl

@@ -70,7 +70,7 @@ function Utils.generate_dataset(b::DynamicVehicleSchedulingBenchmark, dataset_si
     dataset_size = min(dataset_size, length(files))
     return [
         DataSample(;
-            info=Instance(
+            instance=Instance(
                 read_vsp_instance(files[i]);
                 max_requests_per_epoch,
                 Δ_dispatch,

src/DynamicVehicleScheduling/anticipative_solver.jl

@@ -21,13 +21,14 @@ function retrieve_routes_anticipative(
             current_task = task
             while current_task != 1 # < nb_tasks
                 push!(route, current_task)
-                local next_task
+                next_task = -1
                 for i in 1:nb_tasks
                     if isapprox(y[current_task, i, t], 1; atol=0.1)
                         next_task = i
                         break
                     end
                 end
+                @assert next_task != -1 "No next task found from task $current_task at epoch $t"
                 current_task = next_task
             end
             push!(routes[i], route)
@@ -92,14 +93,14 @@ 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 +172,14 @@ 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 +203,7 @@ 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 +224,7 @@ function anticipative_solver(
             compute_features(state, env.instance)
         end
 
-        return DataSample(; info=(; state, reward), y=y_true, x)
+        return DataSample(; y=y_true, x, state, reward)
     end
 
     return obj, dataset

src/DynamicVehicleScheduling/maximizer.jl

@@ -62,13 +62,14 @@ function retrieve_routes(y::AbstractArray, graph::AbstractGraph)
         current_task = task
         while current_task != 1 # < nb_tasks
             push!(route, current_task)
-            local next_task
+            next_task = -1
             for i in outneighbors(graph, current_task)
                 if isapprox(y[current_task, i], 1; atol=0.1)
                     next_task = i
                     break
                 end
             end
+            @assert next_task != -1 "No next task found from task $current_task"
             current_task = next_task
         end
         push!(routes, route)
@@ -93,7 +94,7 @@ function prize_collecting_vsp(
     nb_nodes = nv(graph)
     job_indices = 2:(nb_nodes)
 
-    @variable(model, y[i = 1:nb_nodes, j = 1:nb_nodes; has_edge(graph, i, j)] >= 0)
+    @variable(model, y[i=1:nb_nodes, j=1:nb_nodes; has_edge(graph, i, j)] >= 0)
 
     θ_ext = fill(0.0, location_count(instance))  # no prize for must dispatch requests, only hard constraints
     θ_ext[instance.is_postponable] .= θ
@@ -129,7 +130,9 @@ end
 
 function oracle(θ; instance::DVSPState, kwargs...)
     routes = prize_collecting_vsp(θ; instance=instance, kwargs...)
-    return VSPSolution(routes; max_index=location_count(instance.state_instance)).edge_matrix
+    return VSPSolution(
+        routes; max_index=location_count(instance.state_instance)
+    ).edge_matrix
 end
 
 function g(y; instance, kwargs...)

src/DynamicVehicleScheduling/static_vsp/parsing.jl

@@ -10,14 +10,14 @@ Normalize all time values by the `normalization` parameter.
 function read_vsp_instance(filepath::String; normalization=3600.0, digits=2)
     type = Float64 #rounded ? Int : Float64
     mode = ""
-    local edge_weight_type
-    local edge_weight_format
+    edge_weight_type = ""
+    edge_weight_format = ""
     duration_matrix = Vector{type}[]
     nb_locations = 0
-    local demand
-    local service_time
-    local coordinates
-    local start_time
+    demand = type[]
+    service_time = type[]
+    coordinates = Matrix{type}(undef, 0, 2)
+    start_time = type[]
 
     file = open(filepath, "r")
     for line in eachline(file)

src/StochasticVehicleScheduling/StochasticVehicleScheduling.jl

@@ -67,7 +67,7 @@ end
 function Utils.objective_value(
     ::StochasticVehicleSchedulingBenchmark, sample::DataSample, y::BitVector
 )
-    return evaluate_solution(y, sample.info)
+    return evaluate_solution(y, sample.instance)
 end
 
 """
@@ -98,7 +98,7 @@ function Utils.generate_sample(
     else
         nothing
     end
-    return DataSample(; x, info=instance, y=y_true)
+    return DataSample(; x, instance, y=y_true)
 end
 
 """
@@ -145,11 +145,12 @@ end
 $TYPEDSIGNATURES
 """
 function plot_instance(
-    ::StochasticVehicleSchedulingBenchmark, sample::DataSample{<:Instance{City}}; kwargs...
+    ::StochasticVehicleSchedulingBenchmark, sample::DataSample; kwargs...
 )
-    (; tasks, district_width, width) = sample.info.city
+    @assert hasproperty(sample.instance, :city) "Sample does not contain city information."
+    (; tasks, district_width, width) = sample.instance.city
     ticks = 0:district_width:width
-    max_time = maximum(t.end_time for t in sample.info.city.tasks[1:(end - 1)])
+    max_time = maximum(t.end_time for t in sample.instance.city.tasks[1:(end - 1)])
     fig = plot(;
         xlabel="x",
         ylabel="y",
@@ -204,11 +205,12 @@ end
 $TYPEDSIGNATURES
 """
 function plot_solution(
-    ::StochasticVehicleSchedulingBenchmark, sample::DataSample{<:Instance{City}}; kwargs...
+    ::StochasticVehicleSchedulingBenchmark, sample::DataSample; kwargs...
 )
-    (; tasks, district_width, width) = sample.info.city
+    @assert hasproperty(sample.instance, :city) "Sample does not contain city information."
+    (; tasks, district_width, width) = sample.instance.city
     ticks = 0:district_width:width
-    solution = Solution(sample.y, sample.info)
+    solution = Solution(sample.y, sample.instance)
     path_list = compute_path_list(solution)
     fig = plot(;
         xlabel="x",

src/StochasticVehicleScheduling/instance/instance.jl

@@ -39,8 +39,8 @@ function create_VSP_graph(city::City)
     job_tasks = 2:(city.nb_tasks + 1)
 
     travel_times = [
-        distance(task1.end_point, task2.start_point) for
-        task1 in city.tasks, task2 in city.tasks
+        distance(task1.end_point, task2.start_point) for task1 in city.tasks,
+        task2 in city.tasks
     ]
 
     # Create existing edges

src/StochasticVehicleScheduling/maximizer.jl

@@ -14,7 +14,7 @@ function vsp_maximizer(
     nb_nodes = nv(graph)
     job_indices = 2:(nb_nodes - 1)
 
-    @variable(model, y[i = 1:nb_nodes, j = 1:nb_nodes; has_edge(graph, i, j)], Bin)
+    @variable(model, y[i=1:nb_nodes, j=1:nb_nodes; has_edge(graph, i, j)], Bin)
 
     @objective(
         model,

src/StochasticVehicleScheduling/solution/algorithms/local_search.jl

@@ -11,8 +11,8 @@ function solve_deterministic_VSP(
     (; city, graph) = instance
 
     travel_times = [
-        distance(task1.end_point, task2.start_point) for
-        task1 in city.tasks, task2 in city.tasks
+        distance(task1.end_point, task2.start_point) for task1 in city.tasks,
+        task2 in city.tasks
     ]
 
     model = model_builder()
@@ -21,7 +21,7 @@ function solve_deterministic_VSP(
     nb_nodes = nv(graph)
     job_indices = 2:(nb_nodes - 1)
 
-    @variable(model, x[i = 1:nb_nodes, j = 1:nb_nodes; has_edge(graph, i, j)], Bin)
+    @variable(model, x[i=1:nb_nodes, j=1:nb_nodes; has_edge(graph, i, j)], Bin)
 
     @objective(
         model,

src/Utils/Utils.jl

@@ -35,7 +35,6 @@ export generate_policies
 export generate_anticipative_solution
 
 export plot_data, compute_gap
-export maximizer_kwargs
 export grid_graph, get_path, path_to_matrix
 export neg_tensor, squeeze_last_dims, average_tensor
 export scip_model, highs_model