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112 lines
3.7 KiB
112 lines
3.7 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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import pickle
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import tempfile
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import pyomo.environ as pe
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from miplearn import LearningSolver, BranchPriorityComponent
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from miplearn.problems.knapsack import KnapsackInstance
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from miplearn.solvers import GurobiSolver, CPLEXSolver
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def _get_instance():
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return 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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def test_internal_solver():
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for solver in [GurobiSolver(), CPLEXSolver(presolve=False)]:
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instance = _get_instance()
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model = instance.to_model()
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solver.set_instance(instance, model)
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solver.set_warm_start({
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"x": {
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0: 1.0,
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1: 0.0,
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2: 1.0,
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3: 1.0,
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}
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})
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stats = solver.solve()
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assert stats["Lower bound"] == 1183.0
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assert stats["Upper bound"] == 1183.0
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assert stats["Sense"] == "max"
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assert isinstance(stats["Wallclock time"], float)
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assert isinstance(stats["Nodes"], int)
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solution = solver.get_solution()
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assert solution["x"][0] == 1.0
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assert solution["x"][1] == 0.0
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assert solution["x"][2] == 1.0
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assert solution["x"][3] == 1.0
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stats = solver.solve_lp()
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assert round(stats["Optimal value"], 3) == 1287.923
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solution = solver.get_solution()
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assert round(solution["x"][0], 3) == 1.000
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assert round(solution["x"][1], 3) == 0.923
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assert round(solution["x"][2], 3) == 1.000
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assert round(solution["x"][3], 3) == 0.000
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model.cut = pe.Constraint(expr=model.x[0] <= 0.5)
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solver.add_constraint(model.cut)
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solver.solve_lp()
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assert model.x[0].value == 0.5
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def test_learning_solver():
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instance = _get_instance()
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for mode in ["exact", "heuristic"]:
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for internal_solver in ["cplex", "gurobi", GurobiSolver]:
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solver = LearningSolver(time_limit=300,
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gap_tolerance=1e-3,
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threads=1,
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solver=internal_solver,
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mode=mode)
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solver.solve(instance)
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assert instance.solution["x"][0] == 1.0
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assert instance.solution["x"][1] == 0.0
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assert instance.solution["x"][2] == 1.0
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assert instance.solution["x"][3] == 1.0
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assert instance.lower_bound == 1183.0
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assert instance.upper_bound == 1183.0
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assert round(instance.lp_solution["x"][0], 3) == 1.000
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assert round(instance.lp_solution["x"][1], 3) == 0.923
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assert round(instance.lp_solution["x"][2], 3) == 1.000
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assert round(instance.lp_solution["x"][3], 3) == 0.000
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assert round(instance.lp_value, 3) == 1287.923
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solver.fit([instance])
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solver.solve(instance)
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# Assert solver is picklable
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with tempfile.TemporaryFile() as file:
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pickle.dump(solver, file)
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def test_parallel_solve():
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instances = [_get_instance() for _ in range(10)]
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solver = LearningSolver()
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results = solver.parallel_solve(instances, n_jobs=3)
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assert len(results) == 10
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for instance in instances:
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assert len(instance.solution["x"].keys()) == 4
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def test_add_components():
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solver = LearningSolver(components=[])
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solver.add(BranchPriorityComponent())
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solver.add(BranchPriorityComponent())
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assert len(solver.components) == 1
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assert "BranchPriorityComponent" in solver.components
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