Make lazy constr component compatible with Pyomo+Gurobi

tests/components/lazy/test_mem.py

@@ -10,53 +10,60 @@ from sklearn.neighbors import KNeighborsClassifier
 
 from miplearn.components.lazy.mem import MemorizingLazyComponent
 from miplearn.extractors.abstract import FeaturesExtractor
-from miplearn.problems.tsp import build_tsp_model
+from miplearn.problems.tsp import build_tsp_model_gurobipy, build_tsp_model_pyomo
 from miplearn.solvers.learning import LearningSolver
 
 
 def test_mem_component(
-    tsp_h5: List[str],
+    tsp_gp_h5: List[str],
+    tsp_pyo_h5: List[str],
     default_extractor: FeaturesExtractor,
 ) -> None:
-    clf = Mock(wraps=DummyClassifier())
-    comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
-    comp.fit(tsp_h5)
+    for h5 in [tsp_gp_h5, tsp_pyo_h5]:
+        clf = Mock(wraps=DummyClassifier())
+        comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
+        comp.fit(tsp_gp_h5)
 
-    # Should call fit method with correct arguments
-    clf.fit.assert_called()
-    x, y = clf.fit.call_args.args
-    assert x.shape == (3, 190)
-    assert y.tolist() == [
-        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
-        [1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0],
-        [1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1],
-    ]
+        # Should call fit method with correct arguments
+        clf.fit.assert_called()
+        x, y = clf.fit.call_args.args
+        assert x.shape == (3, 190)
+        assert y.tolist() == [
+            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+            [1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0],
+            [1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1],
+        ]
 
-    # Should store violations
-    assert comp.constrs_ is not None
-    assert comp.n_features_ == 190
-    assert comp.n_targets_ == 22
-    assert len(comp.constrs_) == 22
+        # Should store violations
+        assert comp.constrs_ is not None
+        assert comp.n_features_ == 190
+        assert comp.n_targets_ == 20
+        assert len(comp.constrs_) == 20
 
-    # Call before-mip
-    stats: Dict[str, Any] = {}
-    model = Mock()
-    comp.before_mip(tsp_h5[0], model, stats)
+        # Call before-mip
+        stats: Dict[str, Any] = {}
+        model = Mock()
+        comp.before_mip(tsp_gp_h5[0], model, stats)
 
-    # Should call predict with correct args
-    clf.predict.assert_called()
-    (x_test,) = clf.predict.call_args.args
-    assert x_test.shape == (1, 190)
+        # Should call predict with correct args
+        clf.predict.assert_called()
+        (x_test,) = clf.predict.call_args.args
+        assert x_test.shape == (1, 190)
 
 
 def test_usage_tsp(
-    tsp_h5: List[str],
+    tsp_gp_h5: List[str],
+    tsp_pyo_h5: List[str],
     default_extractor: FeaturesExtractor,
 ) -> None:
-    # Should not crash
-    data_filenames = [f.replace(".h5", ".pkl.gz") for f in tsp_h5]
-    clf = KNeighborsClassifier(n_neighbors=1)
-    comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
-    solver = LearningSolver(components=[comp])
-    solver.fit(data_filenames)
-    solver.optimize(data_filenames[0], build_tsp_model)
+    for (h5, build_model) in [
+        (tsp_pyo_h5, build_tsp_model_pyomo),
+        (tsp_gp_h5, build_tsp_model_gurobipy),
+    ]:
+        data_filenames = [f.replace(".h5", ".pkl.gz") for f in h5]
+        clf = KNeighborsClassifier(n_neighbors=1)
+        comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
+        solver = LearningSolver(components=[comp])
+        solver.fit(data_filenames)
+        stats = solver.optimize(data_filenames[0], build_model)  # type: ignore
+        assert stats["Lazy Constraints: AOT"] > 0

tests/conftest.py

@@ -47,8 +47,13 @@ def multiknapsack_h5(request: Any) -> List[str]:
 
 
 @pytest.fixture()
-def tsp_h5(request: Any) -> List[str]:
-    return _h5_fixture("tsp*.h5", request)
+def tsp_gp_h5(request: Any) -> List[str]:
+    return _h5_fixture("tsp-gp*.h5", request)
+
+
+@pytest.fixture()
+def tsp_pyo_h5(request: Any) -> List[str]:
+    return _h5_fixture("tsp-pyo*.h5", request)
 
 
 @pytest.fixture()

tests/fixtures/gen_tsp.py

@@ -5,7 +5,11 @@ from scipy.stats import uniform, randint
 
 from miplearn.collectors.basic import BasicCollector
 from miplearn.io import write_pkl_gz
-from miplearn.problems.tsp import TravelingSalesmanGenerator, build_tsp_model
+from miplearn.problems.tsp import (
+    TravelingSalesmanGenerator,
+    build_tsp_model_gurobipy,
+    build_tsp_model_pyomo,
+)
 
 np.random.seed(42)
 gen = TravelingSalesmanGenerator(
@@ -16,7 +20,27 @@ gen = TravelingSalesmanGenerator(
     fix_cities=True,
     round=True,
 )
+
 data = gen.generate(3)
-data_filenames = write_pkl_gz(data, dirname(__file__), prefix="tsp-n20-")
+
+params = {"seed": 42, "threads": 1}
+
+# Gurobipy
+data_filenames = write_pkl_gz(data, dirname(__file__), prefix="tsp-gp-n20-")
 collector = BasicCollector()
-collector.collect(data_filenames, build_tsp_model)
+collector.collect(
+    data_filenames,
+    lambda d: build_tsp_model_gurobipy(d, params=params),
+    progress=True,
+    verbose=True,
+)
+
+# Pyomo
+data_filenames = write_pkl_gz(data, dirname(__file__), prefix="tsp-pyo-n20-")
+collector = BasicCollector()
+collector.collect(
+    data_filenames,
+    lambda d: build_tsp_model_pyomo(d, params=params),
+    progress=True,
+    verbose=True,
+)

tests/problems/test_tsp.py

@@ -6,7 +6,7 @@ import numpy as np
 from miplearn.problems.tsp import (
     TravelingSalesmanData,
     TravelingSalesmanGenerator,
-    build_tsp_model,
+    build_tsp_model_gurobipy,
 )
 from scipy.spatial.distance import pdist, squareform
 from scipy.stats import randint, uniform
@@ -51,7 +51,7 @@ def test_tsp() -> None:
             )
         ),
     )
-    model = build_tsp_model(data)
+    model = build_tsp_model_gurobipy(data)
     model.optimize()
     assert model.inner.getAttr("x", model.inner.getVars()) == [
         1.0,