Reorganize files

test/solvers/jump.jl

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+#  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 Test
+using MIPLearn
+using Gurobi
+using PyCall
+using JuMP
+
+miplearn_tests = pyimport("miplearn.solvers.tests")
+traceback = pyimport("traceback")
+
+@testset "JuMPSolver" begin
+    solver = JuMPSolver(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/solvers/learning.jl

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+#  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 "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]
+
+        solver = LearningSolver(Gurobi.Optimizer)
+        instance = JuMPInstance(model)
+        stats = solve!(solver, instance)
+        @test stats["mip_lower_bound"] == 11.0
+        @test length(instance.py.samples) == 1
+        fit!(solver, [instance])
+        solve!(solver, instance)
+    end
+
+    @testset "model without annotations" begin
+        model = Model()
+        @variable(model, x, Bin)
+        @variable(model, y, Bin)
+        @objective(model, Max, x + y)
+        solver = LearningSolver(Gurobi.Optimizer)
+        instance = JuMPInstance(model)
+        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