Implement BenchmarkRunner

test/solvers/learning_test.jl

@@ -8,37 +8,7 @@ using Gurobi
 
 @testset "LearningSolver" begin
     @testset "Model with annotations" begin
-        # Create standard JuMP model
-        weights = [1.0, 2.0, 3.0]
-        prices = [5.0, 6.0, 7.0]
-        capacity = 3.0
-        model = Model()
-
-        n = length(weights)
-        @variable(model, x[1:n], Bin)
-        @objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
-        @constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
-
-        # Add ML information to the model
-        @feature(model, [5.0])
-        @feature(c1, [1.0, 2.0, 3.0])
-        @category(c1, "c1")
-        for i in 1:n
-            @feature(x[i], [weights[i]; prices[i]])
-            @category(x[i], "type-$i")
-        end
-    
-        # Should store ML information
-        @test model.ext[:miplearn][:variable_features][x[1]] == [1.0, 5.0]
-        @test model.ext[:miplearn][:variable_features][x[2]] == [2.0, 6.0]
-        @test model.ext[:miplearn][:variable_features][x[3]] == [3.0, 7.0]
-        @test model.ext[:miplearn][:variable_categories][x[1]] == "type-1"
-        @test model.ext[:miplearn][:variable_categories][x[2]] == "type-2"
-        @test model.ext[:miplearn][:variable_categories][x[3]] == "type-3"
-        @test model.ext[:miplearn][:constraint_features][c1] == [1.0, 2.0, 3.0]
-        @test model.ext[:miplearn][:constraint_categories][c1] == "c1"
-        @test model.ext[:miplearn][:instance_features] == [5.0]
-
+        model = build_knapsack_model()
         solver = LearningSolver(Gurobi.Optimizer)
         instance = JuMPInstance(model)
         stats = solve!(solver, instance)
@@ -49,14 +19,11 @@ using Gurobi
     end
 
     @testset "Model without annotations" begin
-        model = Model()
-        @variable(model, x, Bin)
-        @variable(model, y, Bin)
-        @objective(model, Max, x + y)
+        model = build_knapsack_model()
         solver = LearningSolver(Gurobi.Optimizer)
         instance = JuMPInstance(model)
         stats = solve!(solver, instance)
-        @test stats["mip_lower_bound"] == 2.0
+        @test stats["mip_lower_bound"] == 11.0
     end
 
     @testset "Save and load" begin