MIPLearn v0.3

tests/components/primal/test_mem.py

@@ -0,0 +1,86 @@
+#  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.
+import logging
+from typing import List, Dict, Any
+from unittest.mock import Mock
+
+import numpy as np
+from sklearn.dummy import DummyClassifier
+
+from miplearn.components.primal.actions import SetWarmStart
+from miplearn.components.primal.mem import (
+    MemorizingPrimalComponent,
+    SelectTopSolutions,
+    MergeTopSolutions,
+)
+from miplearn.extractors.abstract import FeaturesExtractor
+
+logger = logging.getLogger(__name__)
+
+
+def test_mem_component(
+    multiknapsack_h5: List[str], default_extractor: FeaturesExtractor
+) -> None:
+    # Create mock classifier
+    clf = Mock(wraps=DummyClassifier())
+
+    # Create and fit component
+    comp = MemorizingPrimalComponent(
+        clf,
+        extractor=default_extractor,
+        constructor=SelectTopSolutions(2),
+        action=SetWarmStart(),
+    )
+    comp.fit(multiknapsack_h5)
+
+    # Should call fit method with correct arguments
+    clf.fit.assert_called()
+    x, y = clf.fit.call_args.args
+    assert x.shape == (3, 100)
+    assert y.tolist() == [0, 1, 2]
+
+    # Should store solutions
+    assert comp.solutions_ is not None
+    assert comp.solutions_.shape == (3, 100)
+    assert comp.bin_var_names_ is not None
+    assert len(comp.bin_var_names_) == 100
+
+    # Call before-mip
+    stats: Dict[str, Any] = {}
+    model = Mock()
+    comp.before_mip(multiknapsack_h5[0], model, stats)
+
+    # Should call predict_proba with correct args
+    clf.predict_proba.assert_called()
+    (x_test,) = clf.predict_proba.call_args.args
+    assert x_test.shape == (1, 100)
+
+    # Should set warm starts
+    model.set_warm_starts.assert_called()
+    names, starts, _ = model.set_warm_starts.call_args.args
+    assert len(names) == 100
+    assert starts.shape == (2, 100)
+    assert np.all(starts[0, :] == comp.solutions_[0, :])
+    assert np.all(starts[1, :] == comp.solutions_[1, :])
+
+
+def test_merge_top_solutions() -> None:
+    solutions = np.array(
+        [
+            [0, 1, 0, 0],
+            [0, 1, 0, 1],
+            [0, 1, 1, 1],
+            [0, 1, 1, 1],
+            [1, 1, 1, 1],
+        ]
+    )
+    y_proba = np.array([0.25, 0.25, 0.25, 0.25, 0])
+    starts = MergeTopSolutions(k=4, thresholds=[0.25, 0.75]).construct(
+        y_proba, solutions
+    )
+    assert starts.shape == (1, 4)
+    assert starts[0, 0] == 0
+    assert starts[0, 1] == 1
+    assert np.isnan(starts[0, 2])
+    assert starts[0, 3] == 1