Reorganize package; add macros

test/modeling/jump_solver_test.jl

@@ -0,0 +1,21 @@
+#  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 Cbc
+using PyCall
+
+miplearn_tests = pyimport("miplearn.solvers.tests")
+
+@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

test/modeling/learning_solver_test.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 Cbc
+
+@testset "macros" begin
+    weights = [1.0, 2.0, 3.0]
+    prices = [5.0, 6.0, 7.0]
+    capacity = 3.0
+
+    # Create standard JuMP model
+    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 machine-learning information
+    @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 variable features
+    @test model.ext[:miplearn][:features][:variables] == Dict(
+        "x[1]" => Dict(
+            :user_features => [1.0, 5.0],
+            :category => "type-1",
+
+        ),
+        "x[2]" => Dict(
+            :user_features => [2.0, 6.0],
+            :category => "type-2",
+        ),
+        "x[3]" => Dict(
+            :user_features => [3.0, 7.0],
+            :category => "type-3",
+        ),
+    )
+
+    # Should store constraint features
+    @test model.ext[:miplearn][:features][:constraints] == Dict(
+        "c1" => Dict(
+            :user_features => [1.0, 2.0, 3.0],
+            :category => "c1",
+        )
+    )
+
+    # Should store instance features
+    @test model.ext[:miplearn][:features][:instance] == Dict(
+        :user_features => [5.0],
+    )
+
+    solver = LearningSolver(optimizer=Cbc.Optimizer)
+    
+    # Should return correct stats
+    stats = solve!(solver, model)
+    @test stats["Lower bound"] == 11.0
+
+    # Should add a sample to the training data
+    @test length(model.ext[:miplearn][:training_samples]) == 1
+    sample = model.ext[:miplearn][:training_samples][1]
+    @test sample["lower_bound"] == 11.0
+    @test sample["solution"]["x[1]"] == 1.0
+
+    fit!(solver, [model])
+
+    solve!(solver, model)
+end