Merge branch 'dev' into feature/replay

test/Project.toml

@@ -7,6 +7,7 @@ version = "0.1.0"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
@@ -17,6 +18,8 @@ Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MIPLearn = "2b1277c3-b477-4c49-a15e-7ba350325c68"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
+SCIP = "82193955-e24f-5292-bf16-6f2c5261a85f"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]

test/src/BB/test_bb.jl

@@ -89,11 +89,7 @@ function bb_run(optimizer_name, optimizer; large=true)
                     BB.ReliabilityBranching(aggregation=:min, collect=true),
                 ]
                     h5 = H5File("$FIXTURES/$instance.h5")
-                    mip_lower_bound = h5.get_scalar("mip_lower_bound")
-                    mip_upper_bound = h5.get_scalar("mip_upper_bound")
-                    mip_sense = h5.get_scalar("mip_sense")
-                    mip_primal_bound =
-                        mip_sense == "min" ? mip_upper_bound : mip_lower_bound
+                    mip_obj_bound = h5.get_scalar("mip_obj_bound")
                     h5.file.close()
 
                     mip = BB.init(optimizer)
@@ -101,10 +97,10 @@ function bb_run(optimizer_name, optimizer; large=true)
                     @info optimizer_name, branch_rule, instance
                     @time BB.solve!(
                         mip,
-                        initial_primal_bound=mip_primal_bound,
-                        print_interval=1,
-                        node_limit=25,
-                        branch_rule=branch_rule,
+                        initial_primal_bound = mip_obj_bound,
+                        print_interval = 1,
+                        node_limit = 25,
+                        branch_rule = branch_rule,
                     )
                 end
             end

test/src/Cuts/tableau/test_gmi.jl

@@ -0,0 +1,23 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using HiGHS
+
+function test_cuts_tableau_gmi()
+    mps_filename = "$BASEDIR/../fixtures/bell5.mps.gz"
+    h5_filename = "$BASEDIR/../fixtures/bell5.h5"
+    collect_gmi(mps_filename, optimizer = HiGHS.Optimizer)
+    h5 = H5File(h5_filename, "r")
+    try
+        cuts_lb = h5.get_array("cuts_lb")
+        cuts_ub = h5.get_array("cuts_ub")
+        cuts_lhs = h5.get_sparse("cuts_lhs")
+        n_cuts = length(cuts_lb)
+        @test n_cuts > 0
+        @test n_cuts == length(cuts_ub)
+        @test cuts_lhs.shape[1] == n_cuts
+    finally
+        h5.close()
+    end
+end

test/src/Cuts/tableau/test_gmi_dual.jl

@@ -0,0 +1,70 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using SCIP
+using HiGHS
+using MIPLearn.Cuts
+
+function test_cuts_tableau_gmi_dual_collect()
+    mps_filename = "$BASEDIR/../fixtures/bell5.mps.gz"
+    h5_filename = "$BASEDIR/../fixtures/bell5.h5"
+    stats = collect_gmi_dual(mps_filename, optimizer = HiGHS.Optimizer)
+    h5 = H5File(h5_filename, "r")
+    try
+        cuts_basis_vars = h5.get_array("cuts_basis_vars")
+        cuts_basis_sizes = h5.get_array("cuts_basis_sizes")
+        cuts_rows = h5.get_array("cuts_rows")
+        @test size(cuts_basis_vars) == (15, 402)
+        @test size(cuts_basis_sizes) == (15, 4)
+        @test size(cuts_rows) == (15,)
+    finally
+        h5.close()
+    end
+end
+
+function test_cuts_tableau_gmi_dual_usage()
+    function build_model(mps_filename)
+        model = read_from_file(mps_filename)
+        set_optimizer(model, SCIP.Optimizer)
+        return JumpModel(model)
+    end
+
+    mps_filename = "$BASEDIR/../fixtures/bell5.mps.gz"
+    h5_filename = "$BASEDIR/../fixtures/bell5.h5"
+    rm(h5_filename, force=true)
+
+    # Run basic collector
+    bc = BasicCollector(write_mps = false, skip_lp = true)
+    bc.collect([mps_filename], build_model)
+
+    # Run dual GMI collector
+    @info "Running dual GMI collector..."
+    collect_gmi_dual(mps_filename, optimizer = HiGHS.Optimizer)
+
+    # # Test expert component
+    # solver = LearningSolver(
+    #     components = [
+    #         ExpertPrimalComponent(action = SetWarmStart()),
+    #         ExpertDualGmiComponent(),
+    #     ],
+    #     skip_lp = true,
+    # )
+    # solver.optimize(mps_filename, build_model)
+
+    # Test kNN component
+    knn = KnnDualGmiComponent(
+        extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
+        k = 2,
+    )
+    knn.fit([h5_filename, h5_filename])
+    solver = LearningSolver(
+        components = [
+            ExpertPrimalComponent(action = SetWarmStart()),
+            knn,
+        ],
+        skip_lp = true,
+    )
+    solver.optimize(mps_filename, build_model)
+    return
+end

test/src/MIPLearnT.jl

@@ -16,10 +16,16 @@ FIXTURES = "$BASEDIR/../fixtures"
 include("fixtures.jl")
 
 include("BB/test_bb.jl")
+include("components/test_cuts.jl")
+include("components/test_lazy.jl")
 include("Cuts/BlackBox/test_cplex.jl")
+include("Cuts/tableau/test_gmi.jl")
+include("Cuts/tableau/test_gmi_dual.jl")
 include("problems/test_setcover.jl")
-include("test_io.jl")
+include("problems/test_stab.jl")
+include("problems/test_tsp.jl")
 include("solvers/test_jump.jl")
+include("test_io.jl")
 include("test_usage.jl")
 
 function runtests()
@@ -27,11 +33,14 @@ function runtests()
         @testset "BB" begin
             test_bb()
         end
-        # test_cuts_blackbox_cplex()
         test_io()
         test_problems_setcover()
+        test_problems_stab()
+        test_problems_tsp()
         test_solvers_jump()
         test_usage()
+        test_cuts()
+        test_lazy()
     end
 end
 

test/src/components/test_cuts.jl

@@ -0,0 +1,41 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using SCIP
+
+function gen_stab()
+    np = pyimport("numpy")
+    uniform = pyimport("scipy.stats").uniform
+    randint = pyimport("scipy.stats").randint
+    np.random.seed(42)
+    gen = MaxWeightStableSetGenerator(
+        w = uniform(10.0, scale = 1.0),
+        n = randint(low = 50, high = 51),
+        p = uniform(loc = 0.5, scale = 0.0),
+        fix_graph = true,
+    )
+    data = gen.generate(1)
+    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "stab-n50-")
+    collector = BasicCollector()
+    collector.collect(
+        data_filenames,
+        data -> build_stab_model_jump(data, optimizer = SCIP.Optimizer),
+        progress = true,
+        verbose = true,
+    )
+end
+
+function test_cuts()
+    data_filenames = ["$BASEDIR/../fixtures/stab-n50-00000.pkl.gz"]
+    clf = pyimport("sklearn.dummy").DummyClassifier()
+    extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
+    comp = MemorizingCutsComponent(clf = clf, extractor = extractor)
+    solver = LearningSolver(components = [comp])
+    solver.fit(data_filenames)
+    model, stats = solver.optimize(
+        data_filenames[1],
+        data -> build_stab_model_jump(data, optimizer = SCIP.Optimizer),
+    )
+    @test stats["Cuts: AOT"] > 0
+end

test/src/components/test_lazy.jl

@@ -0,0 +1,44 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using GLPK
+
+function gen_tsp()
+    np = pyimport("numpy")
+    uniform = pyimport("scipy.stats").uniform
+    randint = pyimport("scipy.stats").randint
+    np.random.seed(42)
+
+    gen = TravelingSalesmanGenerator(
+        x = uniform(loc = 0.0, scale = 1000.0),
+        y = uniform(loc = 0.0, scale = 1000.0),
+        n = randint(low = 20, high = 21),
+        gamma = uniform(loc = 1.0, scale = 0.25),
+        fix_cities = true,
+        round = true,
+    )
+    data = gen.generate(1)
+    data_filenames = write_pkl_gz(data, "$BASEDIR/../fixtures", prefix = "tsp-n20-")
+    collector = BasicCollector()
+    collector.collect(
+        data_filenames,
+        data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
+        progress = true,
+        verbose = true,
+    )
+end
+
+function test_lazy()
+    data_filenames = ["$BASEDIR/../fixtures/tsp-n20-00000.pkl.gz"]
+    clf = pyimport("sklearn.dummy").DummyClassifier()
+    extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"])
+    comp = MemorizingLazyComponent(clf = clf, extractor = extractor)
+    solver = LearningSolver(components = [comp])
+    solver.fit(data_filenames)
+    model, stats = solver.optimize(
+        data_filenames[1],
+        data -> build_tsp_model_jump(data, optimizer = GLPK.Optimizer),
+    )
+    @test stats["Lazy Constraints: AOT"] > 0
+end

test/src/fixtures.jl

@@ -14,5 +14,5 @@ function fixture_setcover_data()
 end
 
 function fixture_setcover_model()
-    return build_setcover_model(fixture_setcover_data())
+    return build_setcover_model_jump(fixture_setcover_data())
 end

test/src/problems/test_setcover.jl

@@ -51,7 +51,7 @@ function test_problems_setcover_model()
     )
 
     h5 = H5File(tempname(), "w")
-    model = build_setcover_model(data)
+    model = build_setcover_model_jump(data)
     model.extract_after_load(h5)
     model.optimize()
     model.extract_after_mip(h5)

test/src/problems/test_stab.jl

@@ -0,0 +1,22 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using PyCall
+using SCIP
+
+function test_problems_stab()
+    nx = pyimport("networkx")
+    data = MaxWeightStableSetData(
+        graph = nx.gnp_random_graph(25, 0.5, seed = 42),
+        weights = repeat([1.0], 25),
+    )
+    h5 = H5File(tempname(), "w")
+    model = build_stab_model_jump(data, optimizer = SCIP.Optimizer)
+    model.extract_after_load(h5)
+    model.optimize()
+    model.extract_after_mip(h5)
+    @test h5.get_scalar("mip_obj_value") == -6
+    @test h5.get_scalar("mip_cuts")[1:20] == "[[0,8,11,13],[0,8,13"
+    h5.close()
+end

test/src/problems/test_tsp.jl

@@ -0,0 +1,22 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using GLPK
+using JuMP
+
+function test_problems_tsp()
+    pdist = pyimport("scipy.spatial.distance").pdist
+    squareform = pyimport("scipy.spatial.distance").squareform
+
+    data = TravelingSalesmanData(
+        n_cities = 6,
+        distances = squareform(
+            pdist([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0], [3.0, 0.0], [0.0, 1.0], [3.0, 1.0]]),
+        ),
+    )
+    model = build_tsp_model_jump(data, optimizer = GLPK.Optimizer)
+    model.optimize()
+    @test objective_value(model.inner) == 8.0
+    return
+end

test/src/test_io.jl

@@ -4,6 +4,7 @@
 
 using MIPLearn
 using JLD2
+using SparseArrays
 
 struct _TestStruct
     n::Int
@@ -35,6 +36,8 @@ function test_h5()
     _test_roundtrip_array(h5, [1, 2, 3])
     _test_roundtrip_array(h5, [1.0, 2.0, 3.0])
     _test_roundtrip_str_array(h5, ["A", "BB", "CCC"])
+    _test_roundtrip_sparse(h5, sparse([1; 2; 3], [1; 2; 3], [1; 2; 3]))
+    # _test_roundtrip_sparse(h5, sparse([1; 2; 3], [1; 2; 3], [1; 2; 3], 4, 4))
     @test h5.get_array("unknown-key") === nothing
     h5.close()
 end
@@ -46,7 +49,7 @@ function test_jld2()
         _TestStruct(2, [1.0, 2.0, 3.0]),
         _TestStruct(3, [3.0, 3.0, 3.0]),
     ]
-    filenames = write_jld2(data, dirname, prefix="obj")
+    filenames = write_jld2(data, dirname, prefix = "obj")
     @test all(
         filenames .==
         ["$dirname/obj00001.jld2", "$dirname/obj00002.jld2", "$dirname/obj00003.jld2"],
@@ -79,3 +82,11 @@ function _test_roundtrip_str_array(h5, original)
     @test recovered !== nothing
     @test all(original .== recovered)
 end
+
+function _test_roundtrip_sparse(h5, original)
+    h5.put_sparse("key", original)
+    recovered = MIPLearn.convert(SparseMatrixCSC, h5.get_sparse("key"))
+    @test recovered !== nothing
+    @test size(original) == size(recovered)
+    @test all(original .== recovered)
+end

test/src/test_usage.jl

@@ -29,13 +29,13 @@ function test_usage()
 
     @debug "Collecting training data..."
     bc = BasicCollector()
-    bc.collect(data_filenames, build_setcover_model)
+    bc.collect(data_filenames, build_setcover_model_jump)
 
     @debug "Training models..."
     solver.fit(data_filenames)
 
     @debug "Solving model..."
-    solver.optimize(data_filenames[1], build_setcover_model)
+    solver.optimize(data_filenames[1], build_setcover_model_jump)
 
     @debug "Checking solution..."
     h5 = H5File(h5_filenames[1])