Implement FileInstance

test/instance/file_test.jl

@@ -0,0 +1,26 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP
+using MIPLearn
+using Gurobi
+
+@testset "FileInstance" begin
+    @testset "solve" begin
+        model = Model()
+        @variable(model, x, Bin)
+        @variable(model, y, Bin)
+        @objective(model, Max, x + y)
+        instance = JuMPInstance(model)
+        filename = tempname()
+        save(filename, instance)
+
+        file_instance = FileInstance(filename)
+        solver = LearningSolver(Gurobi.Optimizer)
+        solve!(solver, file_instance)
+
+        loaded = load_jump_instance(filename)
+        @test length(loaded.py.samples) == 1
+    end    
+end

test/instance/jump_test.jl

@@ -0,0 +1,35 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+@testset "JuMPInstance" begin
+    @testset "save and load" begin
+        # Create basic model
+        model = Model()
+        @variable(model, x, Bin)
+        @variable(model, y, Bin)
+        @objective(model, Max, x + y)
+        @feature(x, [1.0])
+        @category(x, "cat1")
+        @feature(model, [5.0])
+
+        # Solve
+        instance = JuMPInstance(model)
+        solver = LearningSolver(Gurobi.Optimizer)
+        stats = solve!(solver, instance)
+        @test length(instance.py.samples) == 1
+
+        # Save model to file
+        filename = tempname()
+        save(filename, instance)
+        @test isfile(filename)
+
+        # Read model from file
+        loaded = load_jump_instance(filename)
+        x = variable_by_name(loaded.model, "x")
+        @test loaded.model.ext[:miplearn][:variable_features][x] == [1.0]
+        @test loaded.model.ext[:miplearn][:variable_categories][x] == "cat1"
+        @test loaded.model.ext[:miplearn][:instance_features] == [5.0]
+        @test length(loaded.py.samples) == 1
+    end
+end

test/runtests.jl

@@ -8,6 +8,7 @@ using MIPLearn
 MIPLearn.setup_logger()
 
 @testset "MIPLearn" begin
-    include("solvers/jump.jl")
-    include("solvers/learning.jl")
+    include("solvers/jump_test.jl")
+    include("solvers/learning_test.jl")
+    include("instance/file_test.jl")
 end

test/solvers/learning_test.jl

@@ -58,33 +58,4 @@ using Gurobi
         stats = solve!(solver, instance)
     end
 
-    @testset "file model" begin
-        # Create basic model
-        model = Model()
-        @variable(model, x, Bin)
-        @variable(model, y, Bin)
-        @objective(model, Max, x + y)
-        @feature(x, [1.0])
-        @category(x, "cat1")
-        @feature(model, [5.0])
-
-        # Solve
-        instance = JuMPInstance(model)
-        solver = LearningSolver(Gurobi.Optimizer)
-        stats = solve!(solver, instance)
-        @test length(instance.py.samples) == 1
-
-        # Save model to file
-        filename = tempname()
-        save(filename, instance)
-        @test isfile(filename)
-
-        # Read model from file
-        loaded = load_jump_instance(filename)
-        x = variable_by_name(loaded.model, "x")
-        @test loaded.model.ext[:miplearn][:variable_features][x] == [1.0]
-        @test loaded.model.ext[:miplearn][:variable_categories][x] == "cat1"
-        @test loaded.model.ext[:miplearn][:instance_features] == [5.0]
-        @test length(loaded.py.samples) == 1
-    end
 end