Make cuts component compatible with JuMP

test/Project.toml

@@ -15,6 +15,7 @@ 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"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]

test/src/MIPLearnT.jl

@@ -16,10 +16,12 @@ FIXTURES = "$BASEDIR/../fixtures"
 include("fixtures.jl")
 
 include("BB/test_bb.jl")
+include("components/test_cuts.jl")
 include("Cuts/BlackBox/test_cplex.jl")
 include("problems/test_setcover.jl")
-include("test_io.jl")
+include("problems/test_stab.jl")
 include("solvers/test_jump.jl")
+include("test_io.jl")
 include("test_usage.jl")
 
 function runtests()
@@ -27,17 +29,18 @@ function runtests()
         @testset "BB" begin
             test_bb()
         end
-        # test_cuts_blackbox_cplex()
         test_io()
         test_problems_setcover()
+        test_problems_stab()
         test_solvers_jump()
         test_usage()
+        test_cuts()
     end
 end
 
 function format()
-    JuliaFormatter.format(BASEDIR, verbose = true)
-    JuliaFormatter.format("$BASEDIR/../../src", verbose = true)
+    JuliaFormatter.format(BASEDIR, verbose=true)
+    JuliaFormatter.format("$BASEDIR/../../src", verbose=true)
     return
 end
 

test/src/components/test_cuts.jl

@@ -0,0 +1,45 @@
+#  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, "$BASEDIR/../fixtures", prefix="stab-n50-")
+    collector = BasicCollector(write_mps=false)
+    collector.collect(
+        data_filenames,
+        data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
+        progress=true,
+        verbose=true,
+    )
+end
+
+function test_cuts()
+    data_filenames = ["$BASEDIR/../fixtures/stab-n50-0000$i.pkl.gz" for i in 0:0]
+    clf = pyimport("sklearn.neighbors").KNeighborsClassifier(n_neighbors=1)
+    extractor = H5FieldsExtractor(
+        instance_fields=["static_var_obj_coeffs"],
+    )
+    comp = MemorizingCutsComponent(clf=clf, extractor=extractor)
+    solver = LearningSolver(components=[comp])
+    solver.fit(data_filenames)
+    @show comp.n_features_
+    @show comp.n_targets_
+    stats = solver.optimize(
+        data_filenames[1],
+        data -> build_stab_model_jump(data, optimizer=SCIP.Optimizer),
+    )
+    @test stats["Cuts: 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 +1,27 @@
+#  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()
+    test_problems_stab_1()
+    test_problems_stab_2()
+end
+
+function test_problems_stab_1()
+    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/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])