Implement MemorizingCutsComponent; STAB: switch to edge formulation

tests/components/cuts/test_mem.py

@@ -0,0 +1,80 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2023, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import Any, List, Hashable, Dict
+from unittest.mock import Mock
+
+import gurobipy as gp
+import networkx as nx
+from gurobipy import GRB, quicksum
+from sklearn.dummy import DummyClassifier
+from sklearn.neighbors import KNeighborsClassifier
+
+from miplearn.components.cuts.mem import MemorizingCutsComponent
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.problems.stab import build_stab_model
+from miplearn.solvers.gurobi import GurobiModel
+from miplearn.solvers.learning import LearningSolver
+import numpy as np
+
+
+# def test_usage() -> None:
+#     model = _build_cut_model()
+#     solver = LearningSolver(components=[])
+#     solver.optimize(model)
+#     assert model.cuts_ is not None
+#     assert len(model.cuts_) > 0
+#     assert False
+
+
+def test_mem_component(
+    stab_h5: List[str],
+    default_extractor: FeaturesExtractor,
+) -> None:
+    clf = Mock(wraps=DummyClassifier())
+    comp = MemorizingCutsComponent(clf=clf, extractor=default_extractor)
+    comp.fit(stab_h5)
+
+    # Should call fit method with correct arguments
+    clf.fit.assert_called()
+    x, y = clf.fit.call_args.args
+    assert x.shape == (3, 50)
+    assert y.shape == (3, 388)
+    y = y.tolist()
+    assert y[0][:20] == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    assert y[1][:20] == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]
+    assert y[2][:20] == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1]
+
+    # Should store violations
+    assert comp.constrs_ is not None
+    assert comp.n_features_ == 50
+    assert comp.n_targets_ == 388
+    assert len(comp.constrs_) == 388
+
+    # Call before-mip
+    stats: Dict[str, Any] = {}
+    model = Mock()
+    comp.before_mip(stab_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, 50)
+
+    # Should set cuts_aot_
+    assert model.cuts_aot_ is not None
+    assert len(model.cuts_aot_) == 243
+
+
+def test_usage_stab(
+    stab_h5: List[str],
+    default_extractor: FeaturesExtractor,
+) -> None:
+    data_filenames = [f.replace(".h5", ".pkl.gz") for f in stab_h5]
+    clf = KNeighborsClassifier(n_neighbors=1)
+    comp = MemorizingCutsComponent(clf=clf, extractor=default_extractor)
+    solver = LearningSolver(components=[comp])
+    solver.fit(data_filenames)
+    stats = solver.optimize(data_filenames[0], build_stab_model)
+    assert stats["Cuts: AOT"] > 0

tests/components/lazy/test_mem.py

@@ -8,7 +8,7 @@ from unittest.mock import Mock
 from sklearn.dummy import DummyClassifier
 from sklearn.neighbors import KNeighborsClassifier
 
-from miplearn.components.lazy.mem import MemorizingLazyConstrComponent
+from miplearn.components.lazy.mem import MemorizingLazyComponent
 from miplearn.extractors.abstract import FeaturesExtractor
 from miplearn.problems.tsp import build_tsp_model
 from miplearn.solvers.learning import LearningSolver
@@ -19,7 +19,7 @@ def test_mem_component(
     default_extractor: FeaturesExtractor,
 ) -> None:
     clf = Mock(wraps=DummyClassifier())
-    comp = MemorizingLazyConstrComponent(clf=clf, extractor=default_extractor)
+    comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
     comp.fit(tsp_h5)
 
     # Should call fit method with correct arguments
@@ -56,7 +56,7 @@ def test_usage_tsp(
     # Should not crash
     data_filenames = [f.replace(".h5", ".pkl.gz") for f in tsp_h5]
     clf = KNeighborsClassifier(n_neighbors=1)
-    comp = MemorizingLazyConstrComponent(clf=clf, extractor=default_extractor)
+    comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
     solver = LearningSolver(components=[comp])
     solver.fit(data_filenames)
     solver.optimize(data_filenames[0], build_tsp_model)