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50 lines
1.8 KiB
50 lines
1.8 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from miplearn import BranchPriorityComponent, LearningSolver
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from miplearn.problems.knapsack import KnapsackInstance
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import numpy as np
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import tempfile
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def _get_instances():
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return [
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KnapsackInstance(
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weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.,
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),
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] * 2
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def test_branching():
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instances = _get_instances()
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component = BranchPriorityComponent()
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for instance in instances:
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component.after_solve(None, instance, None)
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component.fit(None)
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for key in ["default"]:
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assert key in component.x_train.keys()
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assert key in component.y_train.keys()
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assert component.x_train[key].shape == (8, 4)
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assert component.y_train[key].shape == (8, 1)
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# def test_branch_priority_save_load():
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# state_file = tempfile.NamedTemporaryFile(mode="r")
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# solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
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# solver.parallel_solve(_get_instances(), n_jobs=2)
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# solver.fit()
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# comp = solver.components["branch-priority"]
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# assert comp.x_train["default"].shape == (8, 4)
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# assert comp.y_train["default"].shape == (8, 1)
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# assert "default" in comp.predictors.keys()
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# solver.save_state(state_file.name)
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#
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# solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
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# solver.load_state(state_file.name)
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# comp = solver.components["branch-priority"]
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# assert comp.x_train["default"].shape == (8, 4)
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# assert comp.y_train["default"].shape == (8, 1)
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# assert "default" in comp.predictors.keys()
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