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miplearn/components/tests/test_primal.py

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+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from miplearn import LearningSolver, PrimalSolutionComponent
+from miplearn.problems.knapsack import KnapsackInstance
+import numpy as np
+import tempfile
+
+
+def _get_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
+        ),
+    ] * 5
+    models = [inst.to_model() for inst in instances]
+    solver = LearningSolver()
+    for i in range(len(instances)):
+        solver.solve(instances[i], models[i])
+    return instances, models
+
+
+def test_predict():
+    instances, models = _get_instances()
+    comp = PrimalSolutionComponent()
+    comp.fit(instances)
+    solution = comp.predict(instances[0], models[0])
+    assert models[0].x in solution.keys()
+    assert solution[models[0].x][0] == 1
+    assert solution[models[0].x][1] == 1
+    assert solution[models[0].x][2] == 1
+    assert solution[models[0].x][3] == 1
+
+# def test_warm_start_save_load():
+#     state_file = tempfile.NamedTemporaryFile(mode="r")
+#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+#     solver.parallel_solve(_get_instances(), n_jobs=2)
+#     solver.fit()
+#     comp = solver.components["warm-start"]
+#     assert comp.x_train["default"].shape == (8, 6)
+#     assert comp.y_train["default"].shape == (8, 2)
+#     assert ("default", 0) in comp.predictors.keys()
+#     assert ("default", 1) in comp.predictors.keys()
+#     solver.save_state(state_file.name)
+    
+#     solver.solve(_get_instances()[0])
+    
+#     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+#     solver.load_state(state_file.name)
+#     comp = solver.components["warm-start"]
+#     assert comp.x_train["default"].shape == (8, 6)
+#     assert comp.y_train["default"].shape == (8, 2)
+#     assert ("default", 0) in comp.predictors.keys()
+#     assert ("default", 1) in comp.predictors.keys()