More tests and bugfix

Author: BatyLeo

src/DecisionFocusedLearningBenchmarks.jl

@@ -45,6 +45,7 @@ export AbstractBenchmark, DataSample
 export generate_dataset
 export generate_statistical_model
 export generate_maximizer, maximizer_kwargs
+export objective_value
 export plot_data, plot_instance, plot_solution
 export compute_gap
 

src/PortfolioOptimization/PortfolioOptimization.jl

@@ -2,12 +2,12 @@ module PortfolioOptimization
 
 using ..Utils
 using DocStringExtensions: TYPEDEF, TYPEDFIELDS, TYPEDSIGNATURES
-using Distributions
+using Distributions: Uniform, Bernoulli
 using Flux: Chain, Dense
-using Ipopt
-using JuMP
-using LinearAlgebra
-using Random
+using Ipopt: Ipopt
+using JuMP: @variable, @objective, @constraint, optimize!, value
+using LinearAlgebra: I
+using Random: MersenneTwister
 
 """
 $TYPEDEF

src/StochasticVehicleScheduling/solution/solution.jl

@@ -48,12 +48,12 @@ $TYPEDSIGNATURES
 Create a Solution from a BitVector value.
 """
 function Solution(value::BitVector, instance::Instance)
-    graph = instance.graph
+    (; graph) = instance
     nb_tasks = nv(graph)
     is_selected = falses(nb_tasks, nb_tasks)
     for (i, edge) in enumerate(edges(graph))
         if value[i]
-            is_selected[edge.src, edge.dst] = true
+            is_selected[src(edge), dst(edge)] = true
         end
     end
 
@@ -112,7 +112,7 @@ function solution_from_JuMP_array(x::AbstractArray, graph::AbstractGraph)
     sol = falses(ne(graph)) # init
 
     for (a, edge) in enumerate(edges(graph))
-        if x[edge.src, edge.dst] == 1
+        if x[src(edge), dst(edge)] >= 0.5
             sol[a] = true
         end
     end
@@ -131,8 +131,7 @@ function path_solution_from_JuMP_array(x::AbstractArray, graph::AbstractGraph)
         while current_task < nb_tasks
             sol[v_index, current_task - 1] = true
             next_tasks = [
-                i for i in outneighbors(graph, current_task) if
-                isapprox(x[current_task, i], 1; atol=0.1)
+                i for i in outneighbors(graph, current_task) if x[current_task, i] >= 0.5
             ]
             # TODO : there is a more efficient way to search for next task (but more dangerous)
             if length(next_tasks) == 1

src/Utils/Utils.jl

@@ -23,5 +23,6 @@ export maximizer_kwargs
 export grid_graph, get_path, path_to_matrix
 export neg_tensor, squeeze_last_dims, average_tensor
 export scip_model, highs_model
+export objective_value
 
 end

test/vsp.jl

@@ -10,7 +10,7 @@
     dataset = generate_dataset(b, N; seed=0)
     mip_dataset = generate_dataset(b, N; seed=0, algorithm=compact_mip)
     mipl_dataset = generate_dataset(b, N; seed=0, algorithm=compact_linearized_mip)
-    miplc_dataset = generate_dataset(b, N; seed=0, algorithm=local_search)
+    local_search_dataset = generate_dataset(b, N; seed=0, algorithm=local_search)
     @test length(dataset) == N
 
     figure_1 = plot_instance(b, dataset[1])
@@ -22,6 +22,14 @@
     model = generate_statistical_model(b)
 
     gap = compute_gap(b, dataset, model, maximizer)
+    gap_mip = compute_gap(b, mip_dataset, model, maximizer)
+    gap_mipl = compute_gap(b, mipl_dataset, model, maximizer)
+    gap_local_search = compute_gap(b, local_search_dataset, model, maximizer)
+
+    @test gap >= 0 && gap_mip >= 0 && gap_mipl >= 0 && gap_local_search >= 0
+    @test gap_mip ≈ gap_mipl rtol = 1e-2
+    @test gap_mip >= gap_local_search
+    @test gap_mip >= gap
 
     for sample in dataset
         x = sample.x