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)

tests/components/primal/test_mem.py

@@ -20,7 +20,8 @@ logger = logging.getLogger(__name__)
 
 
 def test_mem_component(
-    multiknapsack_h5: List[str], default_extractor: FeaturesExtractor
+    multiknapsack_h5: List[str],
+    default_extractor: FeaturesExtractor,
 ) -> None:
     # Create mock classifier
     clf = Mock(wraps=DummyClassifier())

tests/conftest.py

@@ -8,13 +8,18 @@ from typing import List
 
 import pytest
 
-from miplearn.extractors.fields import H5FieldsExtractor
 from miplearn.extractors.abstract import FeaturesExtractor
+from miplearn.extractors.fields import H5FieldsExtractor
 
 
 @pytest.fixture()
 def multiknapsack_h5() -> List[str]:
-    return sorted(glob(f"{dirname(__file__)}/fixtures/multiknapsack*.h5"))
+    return sorted(glob(f"{dirname(__file__)}/fixtures/multiknapsack-n100*.h5"))
+
+
+@pytest.fixture()
+def tsp_h5() -> List[str]:
+    return sorted(glob(f"{dirname(__file__)}/fixtures/tsp-n20*.h5"))
 
 
 @pytest.fixture()

tests/fixtures/gen_tsp.py

@@ -0,0 +1,22 @@
+from os.path import dirname
+
+import numpy as np
+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
+
+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(3)
+data_filenames = write_pkl_gz(data, dirname(__file__), prefix="tsp-n20-")
+collector = BasicCollector()
+collector.collect(data_filenames, build_tsp_model)