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202 lines
7.0 KiB
202 lines
7.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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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from warnings import warn
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import pyomo.environ as pe
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from miplearn.solvers import _RedirectOutput
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.pyomo.base import BasePyomoSolver
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from . import (
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_get_knapsack_instance,
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get_internal_solvers,
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)
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from ..fixtures.infeasible import get_infeasible_instance
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logger = logging.getLogger(__name__)
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def test_redirect_output() -> None:
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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() -> None:
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for solver in get_internal_solvers():
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logger.info("Solver: %s" % solver)
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instance = _get_knapsack_instance(solver)
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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({"x[0]": 1.0, "x[1]": 0.0, "x[2]": 0.0, "x[3]": 1.0})
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stats = solver.solve(tee=True)
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if stats["Warm start value"] is not None:
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assert stats["Warm start value"] == 725.0
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else:
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warn(f"{solver.__class__.__name__} should set warm start value")
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solver.set_warm_start({"x[0]": 1.0, "x[1]": 1.0, "x[2]": 1.0, "x[3]": 1.0})
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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({"x[0]": 1.0, "x[1]": 0.0, "x[2]": 0.0, "x[3]": 1.0})
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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() -> None:
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for solver in get_internal_solvers():
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logger.info("Solver: %s" % solver)
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instance = _get_knapsack_instance(solver)
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model = instance.to_model()
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solver.set_instance(instance, model)
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assert solver.get_variable_names() == ["x[0]", "x[1]", "x[2]", "x[3]"]
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lp_stats = solver.solve_lp()
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assert not solver.is_infeasible()
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assert lp_stats["LP value"] is not None
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assert round(lp_stats["LP value"], 3) == 1287.923
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assert len(lp_stats["LP log"]) > 100
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solution = solver.get_solution()
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assert solution is not None
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assert solution["x[0]"] is not None
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assert solution["x[1]"] is not None
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assert solution["x[2]"] is not None
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assert solution["x[3]"] is not None
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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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mip_stats = solver.solve(tee=True)
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assert not solver.is_infeasible()
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assert len(mip_stats["MIP log"]) > 100
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assert mip_stats["Lower bound"] == 1183.0
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assert mip_stats["Upper bound"] == 1183.0
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assert mip_stats["Sense"] == "max"
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assert isinstance(mip_stats["Wallclock time"], float)
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solution = solver.get_solution()
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assert solution is not None
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assert solution["x[0]"] is not None
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assert solution["x[1]"] is not None
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assert solution["x[2]"] is not None
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assert solution["x[3]"] is not None
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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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# Add a brand new constraint
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if isinstance(solver, BasePyomoSolver):
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model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
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solver.add_constraint(model.cut)
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elif isinstance(solver, GurobiSolver):
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x = model.getVarByName("x[0]")
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solver.add_constraint(x <= 0.0, name="cut")
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else:
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raise Exception("Illegal state")
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# New constraint should affect solution and should be listed in
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# constraint ids
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assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
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stats = solver.solve()
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assert stats["Lower bound"] == 1030.0
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assert solver.get_sense() == "max"
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assert solver.get_constraint_sense("cut") == "<"
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assert solver.get_constraint_sense("eq_capacity") == "<"
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# Verify slacks
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assert solver.get_inequality_slacks() == {
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"cut": 0.0,
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"eq_capacity": 3.0,
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}
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if isinstance(solver, GurobiSolver):
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# Extract the new constraint
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cobj = solver.extract_constraint("cut")
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# New constraint should no longer affect solution and should no longer
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# be listed in constraint ids
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assert solver.get_constraint_ids() == ["eq_capacity"]
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stats = solver.solve()
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assert stats["Lower bound"] == 1183.0
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# New constraint should not be satisfied by current solution
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assert not solver.is_constraint_satisfied(cobj)
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# Re-add constraint
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solver.add_constraint(cobj)
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# Constraint should affect solution again
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assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
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stats = solver.solve()
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assert stats["Lower bound"] == 1030.0
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# New constraint should now be satisfied
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assert solver.is_constraint_satisfied(cobj)
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# Relax problem and make cut into an equality constraint
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solver.relax()
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solver.set_constraint_sense("cut", "=")
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stats = solver.solve()
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assert stats["Lower bound"] is not None
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assert round(stats["Lower bound"]) == 1030.0
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assert round(solver.get_dual("eq_capacity")) == 0.0
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def test_relax() -> None:
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for solver in get_internal_solvers():
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instance = _get_knapsack_instance(solver)
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solver.set_instance(instance)
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solver.relax()
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stats = solver.solve()
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assert stats["Lower bound"] is not None
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assert round(stats["Lower bound"]) == 1288.0
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def test_infeasible_instance() -> None:
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for solver in get_internal_solvers():
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instance = get_infeasible_instance(solver)
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solver.set_instance(instance)
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mip_stats = solver.solve()
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assert solver.is_infeasible()
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assert solver.get_solution() is None
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assert mip_stats["Upper bound"] is None
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assert mip_stats["Lower bound"] is None
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lp_stats = solver.solve_lp()
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assert solver.get_solution() is None
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assert lp_stats["LP value"] is None
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def test_iteration_cb() -> None:
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for solver in get_internal_solvers():
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logger.info("Solver: %s" % solver)
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instance = _get_knapsack_instance(solver)
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solver.set_instance(instance)
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count = 0
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def custom_iteration_cb() -> bool:
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nonlocal count
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count += 1
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return count < 5
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solver.solve(iteration_cb=custom_iteration_cb)
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assert count == 5
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