Implement MemorizingLazyConstrComponent

tests/components/lazy/test_mem.py

@@ -0,0 +1,62 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import List, Dict, Any
+from unittest.mock import Mock
+
+from sklearn.dummy import DummyClassifier
+from sklearn.neighbors import KNeighborsClassifier
+
+from miplearn.components.lazy.mem import MemorizingLazyConstrComponent
+from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.problems.tsp import build_tsp_model
+from miplearn.solvers.learning import LearningSolver
+
+
+def test_mem_component(
+    tsp_h5: List[str],
+    default_extractor: FeaturesExtractor,
+) -> None:
+    clf = Mock(wraps=DummyClassifier())
+    comp = MemorizingLazyConstrComponent(clf=clf, extractor=default_extractor)
+    comp.fit(tsp_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 store violations
+    assert comp.violations_ is not None
+    assert comp.n_features_ == 190
+    assert comp.n_targets_ == 22
+    assert len(comp.violations_) == 22
+
+    # Call before-mip
+    stats: Dict[str, Any] = {}
+    model = Mock()
+    comp.before_mip(tsp_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)
+
+
+def test_usage_tsp(
+    tsp_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 = MemorizingLazyConstrComponent(clf=clf, extractor=default_extractor)
+    solver = LearningSolver(components=[comp])
+    solver.fit(data_filenames)
+    solver.optimize(data_filenames[0], build_tsp_model)