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116 lines
3.4 KiB
116 lines
3.4 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 logging
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from io import StringIO
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import pyomo.environ as pe
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from miplearn import BasePyomoSolver
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from miplearn.problems.knapsack import ChallengeA
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from miplearn.solvers import RedirectOutput
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from . import _get_instance, _get_internal_solvers
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logger = logging.getLogger(__name__)
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def test_redirect_output():
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import sys
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original_stdout = sys.stdout
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io = StringIO()
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with RedirectOutput([io]):
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print("Hello world")
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assert sys.stdout == original_stdout
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assert io.getvalue() == "Hello world\n"
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def test_internal_solver_warm_starts():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_instance(solver_class)
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model = instance.to_model()
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solver = solver_class()
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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: 0.0,
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3: 1.0,
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}
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})
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stats = solver.solve(tee=True)
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assert stats["Warm start value"] == 725.0
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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: 1.0,
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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(tee=True)
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assert stats["Warm start value"] is None
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solver.fix({
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"x": {
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0: 1.0,
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1: 0.0,
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2: 0.0,
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3: 1.0,
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}
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})
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stats = solver.solve(tee=True)
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assert stats["Lower bound"] == 725.0
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assert stats["Upper bound"] == 725.0
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def test_internal_solver():
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for solver_class in _get_internal_solvers():
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logger.info("Solver: %s" % solver_class)
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instance = _get_instance(solver_class)
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model = instance.to_model()
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solver = solver_class()
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solver.set_instance(instance, model)
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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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stats = solver.solve(tee=True)
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assert len(stats["Log"]) > 100
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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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if isinstance(solver, BasePyomoSolver):
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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_node_count():
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# for solver in _get_internal_solvers():
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# challenge = ChallengeA()
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# solver.set_time_limit(1)
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# solver.set_instance(challenge.test_instances[0])
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# stats = solver.solve(tee=True)
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# assert stats["Nodes"] > 1
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