try to fix tests in CI

Author: BatyLeo

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"
@@ -65,22 +68,3 @@ SparseArrays = "1"
 Statistics = "1"
 StatsBase = "0.34.4"
 julia = "1.10"
-
-[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"]

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!

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/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/policy.jl

@@ -38,17 +38,17 @@ function evaluate_policy!(
         reset!(env; reset_rng=true, seed=seed)
     end
     total_reward = 0.0
-    local labeled_dataset
+    labeled_dataset = DataSample[]
     while !is_terminated(env)
         y = policy(env; kwargs...)
         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)
-        if @isdefined labeled_dataset
-            push!(labeled_dataset, sample)
+        if isempty(labeled_dataset)
+            labeled_dataset = typeof(sample)[sample]
         else
-            labeled_dataset = [sample]
+            push!(labeled_dataset, sample)
         end
         total_reward += reward
     end

test/Project.toml

@@ -0,0 +1,25 @@
+[deps]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+DecisionFocusedLearningBenchmarks = "2fbe496a-299b-4c81-bab5-c44dfc55cf20"
+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"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
+UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[sources]
+DecisionFocusedLearningBenchmarks = {path = ".."}
+
+[compat]
+Aqua = "0.8.14"
+Test = "1"

test/argmax.jl

@@ -1,4 +1,4 @@
-@testitem "Argmax" begin
+@testset "Argmax" begin
     using DecisionFocusedLearningBenchmarks
 
     instance_dim = 10