style: bump JuliaFormatter to v2

docs/src/tutorials/warcraft_tutorial.jl

@@ -72,7 +72,7 @@ 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) for (; x, y) in train_dataset) / length(train_dataset)
+        return 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)

src/DynamicVehicleScheduling/anticipative_solver.jl

@@ -93,14 +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
         )
     )
 
@@ -172,14 +172,12 @@ 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]
@@ -203,7 +201,8 @@ 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!

src/DynamicVehicleScheduling/maximizer.jl

@@ -94,7 +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
@@ -130,9 +130,7 @@ 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/StochasticVehicleScheduling/instance/instance.jl

@@ -51,8 +51,8 @@ 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/data_sample.jl

@@ -184,7 +184,7 @@ Transform the features in the dataset.
 function StatsBase.transform(t, dataset::AbstractVector{<:DataSample})
     return map(dataset) do d
         (; context, extra, x, θ, y) = d
-        DataSample(; x=StatsBase.transform(t, x), θ, y, context..., extra)
+        return DataSample(; x=StatsBase.transform(t, x), θ, y, context..., extra)
     end
 end
 
@@ -207,7 +207,7 @@ Reconstruct the features in the dataset.
 function StatsBase.reconstruct(t, dataset::AbstractVector{<:DataSample})
     return map(dataset) do d
         (; context, extra, x, θ, y) = d
-        DataSample(StatsBase.reconstruct(t, x), θ, y, context, extra)
+        return DataSample(StatsBase.reconstruct(t, x), θ, y, context, extra)
     end
 end
 

test/Project.toml

@@ -22,5 +22,5 @@ DecisionFocusedLearningBenchmarks = {path = ".."}
 
 [compat]
 Aqua = "0.8.14"
-JuliaFormatter = "1"
+JuliaFormatter = "2"
 Test = "1"

test/contextual_stochastic_argmax.jl

@@ -94,7 +94,7 @@ end
     maximizer = generate_maximizer(saa)
     labeled = map(dataset) do s
         y_saa = maximizer(mean(s.scenarios))
-        DataSample(; s.context..., x=s.x, y=y_saa, extra=s.extra)
+        return DataSample(; s.context..., x=s.x, y=y_saa, extra=s.extra)
     end
     @test sum(first(labeled).y) ≈ 1.0