Use compact variable and constraint features

test/Project.toml

@@ -4,6 +4,7 @@
 
 [deps]
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
+Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
 JSON2 = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"

test/modeling/jump_solver_test.jl

@@ -4,18 +4,22 @@
 
 using Test
 using MIPLearn
-using Cbc
+using Gurobi
 using PyCall
+using JuMP
 
 miplearn_tests = pyimport("miplearn.solvers.tests")
+traceback = pyimport("traceback")
 
 @testset "JuMPSolver" begin
-    model = MIPLearn.knapsack_model(
-        [23., 26., 20., 18.],
-        [505., 352., 458., 220.],
-        67.0,
-    )
-    instance = JuMPInstance(model)
-    solver = JuMPSolver(optimizer=Cbc.Optimizer)
-    miplearn_tests.test_internal_solver(solver, instance, model)
-end
+    solver = JuMPSolver(optimizer=Gurobi.Optimizer)
+    try  
+        miplearn_tests.run_internal_solver_tests(solver)
+    catch e
+        if isa(e, PyCall.PyError)
+            printstyled("Uncaught Python exception:\n", bold=true, color=:red)
+            traceback.print_exception(e.T, e.val, e.traceback)
+        end
+        rethrow()
+    end
+end

test/runtests.jl

@@ -8,6 +8,6 @@ using MIPLearn
 MIPLearn.setup_logger()
 
 @testset "MIPLearn" begin
-    #include("modeling/jump_solver_test.jl")
-    include("modeling/learning_solver_test.jl")
-end
+    include("modeling/jump_solver_test.jl")
+    #include("modeling/learning_solver_test.jl")
+end