MIPLearn v0.3

tests/components/primal/test_expert.py

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+#  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 miplearn.components.primal.actions import SetWarmStart, FixVariables
+from miplearn.components.primal.expert import ExpertPrimalComponent
+
+
+def test_expert(multiknapsack_h5: List[str]) -> None:
+    model = Mock()
+    stats: Dict[str, Any] = {}
+    comp = ExpertPrimalComponent(action=SetWarmStart())
+    comp.before_mip(multiknapsack_h5[0], model, stats)
+    model.set_warm_starts.assert_called()
+    names, starts, _ = model.set_warm_starts.call_args.args
+    assert names.shape == (100,)
+    assert starts.shape == (1, 100)
+
+    comp = ExpertPrimalComponent(action=FixVariables())
+    comp.before_mip(multiknapsack_h5[0], model, stats)
+    model.fix_variables.assert_called()
+    names, v, _ = model.fix_variables.call_args.args
+    assert names.shape == (100,)
+    assert v.shape == (100,)

tests/components/primal/test_indep.py

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+#  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, call
+
+from sklearn.dummy import DummyClassifier
+
+from miplearn.components.primal.actions import SetWarmStart
+from miplearn.components.primal.indep import IndependentVarsPrimalComponent
+from miplearn.extractors.fields import H5FieldsExtractor
+
+
+def test_indep(multiknapsack_h5: List[str]) -> None:
+    # Create and fit component
+    clone_fn = Mock(return_value=Mock(wraps=DummyClassifier()))
+    comp = IndependentVarsPrimalComponent(
+        base_clf="dummy",
+        extractor=H5FieldsExtractor(var_fields=["lp_var_values"]),
+        clone_fn=clone_fn,
+        action=SetWarmStart(),
+    )
+    comp.fit(multiknapsack_h5)
+
+    # Should call clone 100 times and store the 100 classifiers
+    clone_fn.assert_has_calls([call("dummy") for _ in range(100)])
+    assert len(comp.clf_) == 100
+
+    for v in [b"x[0]", b"x[1]"]:
+        # Should pass correct data to fit
+        comp.clf_[v].fit.assert_called()
+        x, y = comp.clf_[v].fit.call_args.args
+        assert x.shape == (3, 1)
+        assert y.shape == (3,)
+
+    # Call before-mip
+    stats: Dict[str, Any] = {}
+    model = Mock()
+    comp.before_mip(multiknapsack_h5[0], model, stats)
+
+    # Should call predict with correct args
+    for v in [b"x[0]", b"x[1]"]:
+        comp.clf_[v].predict.assert_called()
+        (x_test,) = comp.clf_[v].predict.call_args.args
+        assert x_test.shape == (1, 1)
+
+    # 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 == (1, 100)

tests/components/primal/test_joint.py

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+#  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 miplearn.components.primal.actions import SetWarmStart
+from miplearn.components.primal.joint import JointVarsPrimalComponent
+from miplearn.extractors.fields import H5FieldsExtractor
+
+
+def test_joint(multiknapsack_h5: List[str]) -> None:
+    # Create mock classifier
+    clf = Mock(wraps=DummyClassifier())
+
+    # Create and fit component
+    comp = JointVarsPrimalComponent(
+        clf=clf,
+        extractor=H5FieldsExtractor(instance_fields=["static_var_obj_coeffs"]),
+        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.shape == (3, 100)
+
+    # Call before-mip
+    stats: Dict[str, Any] = {}
+    model = Mock()
+    comp.before_mip(multiknapsack_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, 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 == (1, 100)

tests/components/primal/test_mem.py

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+#  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