Replace Gurobi by Clp in most tests

test/instance/file_test.jl

@@ -4,7 +4,7 @@
 
 using JuMP
 using MIPLearn
-using Gurobi
+using Cbc
 
 
 @testset "FileInstance" begin
@@ -15,7 +15,7 @@ using Gurobi
         save(filename, instance)
 
         file_instance = FileInstance(filename)
-        solver = LearningSolver(Gurobi.Optimizer)
+        solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, file_instance)
 
         loaded = load_instance(filename)

test/runtests.jl

@@ -8,9 +8,9 @@ using MIPLearn
 MIPLearn.setup_logger()
 
 @testset "MIPLearn" begin
-    # include("fixtures/knapsack.jl")
+    include("fixtures/knapsack.jl")
     include("solvers/jump_test.jl")
-    # include("solvers/learning_test.jl")
-    # include("instance/file_test.jl")
-    # include("utils/benchmark_test.jl")
+    include("solvers/learning_test.jl")
+    include("instance/file_test.jl")
+    include("utils/benchmark_test.jl")
 end

test/solvers/learning_test.jl

@@ -2,14 +2,14 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+using Cbc
 using JuMP
 using MIPLearn
-using Gurobi
 
 @testset "LearningSolver" begin
     @testset "Model with annotations" begin
         model = build_knapsack_model()
-        solver = LearningSolver(Gurobi.Optimizer)
+        solver = LearningSolver(Cbc.Optimizer)
         instance = JuMPInstance(model)
         stats = solve!(solver, instance)
         @test stats["mip_lower_bound"] == 11.0
@@ -20,14 +20,14 @@ using Gurobi
 
     @testset "Model without annotations" begin
         model = build_knapsack_model()
-        solver = LearningSolver(Gurobi.Optimizer)
+        solver = LearningSolver(Cbc.Optimizer)
         instance = JuMPInstance(model)
         stats = solve!(solver, instance)
         @test stats["mip_lower_bound"] == 11.0
     end
 
     @testset "Save and load" begin
-        solver = LearningSolver(Gurobi.Optimizer)
+        solver = LearningSolver(Cbc.Optimizer)
         solver.py.components = "Placeholder"
         filename = tempname()
         save(filename, solver)
@@ -38,7 +38,7 @@ using Gurobi
 
     @testset "Discard output" begin
         instance = build_knapsack_file_instance()
-        solver = LearningSolver(Gurobi.Optimizer)
+        solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, instance, discard_output=true)
         loaded = load_instance(instance.filename)
         @test length(loaded.py.samples) == 0

test/utils/benchmark_test.jl

@@ -2,19 +2,19 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+using Cbc
 using CSV
 using DataFrames
-using Gurobi
 
 
 @testset "BenchmarkRunner" begin
-    # Initialie instances and generate training data
+    # Initialize instances and generate training data
     instances = [
         build_knapsack_file_instance(),
         build_knapsack_file_instance(),
     ]
     parallel_solve!(
-        LearningSolver(Gurobi.Optimizer),
+        LearningSolver(Cbc.Optimizer),
         instances,
     )
 
@@ -22,14 +22,14 @@ using Gurobi
     benchmark = BenchmarkRunner(
         solvers=Dict(
             "baseline" => LearningSolver(
-                Gurobi.Optimizer,
+                Cbc.Optimizer,
                 components=[],
             ),
             "ml-exact" => LearningSolver(
-                Gurobi.Optimizer,
+                Cbc.Optimizer,
             ),
             "ml-heur" => LearningSolver(
-                Gurobi.Optimizer,
+                Cbc.Optimizer,
                 mode="heuristic",
             ),
         ),