Make lazy constraints compatible with JuMP

test/Project.toml

@@ -5,6 +5,7 @@ version = "0.1.0"
 
 [deps]
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
+GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"

test/src/MIPLearnT.jl

@@ -17,9 +17,11 @@ 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("problems/test_setcover.jl")
 include("problems/test_stab.jl")
+include("problems/test_tsp.jl")
 include("solvers/test_jump.jl")
 include("test_io.jl")
 include("test_usage.jl")
@@ -32,6 +34,7 @@ function runtests()
         test_io()
         test_problems_setcover()
         test_problems_stab()
+        test_problems_tsp()
         test_solvers_jump()
         test_usage()
         test_cuts()

test/src/components/test_cuts.jl

@@ -27,7 +27,7 @@ function gen_stab()
 end
 
 function test_cuts()
-    data_filenames = ["$BASEDIR/../fixtures/stab-n50-0000$i.pkl.gz" for i in 0:0]
+    data_filenames = ["$BASEDIR/../fixtures/stab-n50-00000.pkl.gz"]
     clf = pyimport("sklearn.dummy").DummyClassifier()
     extractor = H5FieldsExtractor(
         instance_fields=["static_var_obj_coeffs"],

test/src/components/test_lazy.jl

@@ -0,0 +1,46 @@
+#  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, "$BASEDIR/../fixtures", prefix="tsp-n20-")
+    collector = BasicCollector(write_mps=false)
+    collector.collect(
+        data_filenames,
+        data -> build_tsp_model_jump(data, optimizer=GLPK.Optimizer),
+        progress=true,
+        verbose=true,
+    )
+end
+
+function test_lazy()
+    data_filenames = ["$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)
+    stats = solver.optimize(
+        data_filenames[1],
+        data -> build_tsp_model_jump(data, optimizer=GLPK.Optimizer),
+    )
+    @test stats["Lazy Constraints: AOT"] > 0
+end

test/src/problems/test_tsp.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 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_usage.jl

@@ -13,22 +13,22 @@ function test_usage()
 
     @debug "Setting up LearningSolver..."
     solver = LearningSolver(
-        components = [
+        components=[
             IndependentVarsPrimalComponent(
-                base_clf = SingleClassFix(
+                base_clf=SingleClassFix(
                     MinProbabilityClassifier(
-                        base_clf = LogisticRegression(),
-                        thresholds = [0.95, 0.95],
+                        base_clf=LogisticRegression(),
+                        thresholds=[0.95, 0.95],
                     ),
                 ),
-                extractor = AlvLouWeh2017Extractor(),
-                action = SetWarmStart(),
+                extractor=AlvLouWeh2017Extractor(),
+                action=SetWarmStart(),
             ),
         ],
     )
 
     @debug "Collecting training data..."
-    bc = BasicCollector()
+    bc = BasicCollector(write_mps=false)
     bc.collect(data_filenames, build_setcover_model_jump)
 
     @debug "Training models..."