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317 lines
10 KiB
317 lines
10 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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from typing import Any, Dict, List
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import numpy as np
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from miplearn.features import Constraint, Variable, VariableFeatures
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from miplearn.solvers.internal import InternalSolver
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inf = float("inf")
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# NOTE:
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# This file is in the main source folder, so that it can be called from Julia.
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def _round_constraints(constraints: Dict[str, Constraint]) -> Dict[str, Constraint]:
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for (cname, c) in constraints.items():
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for attr in ["slack", "dual_value"]:
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if getattr(c, attr) is not None:
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setattr(c, attr, round(getattr(c, attr), 6))
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return constraints
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def _round_variables(vars: Dict[str, Variable]) -> Dict[str, Variable]:
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for (cname, c) in vars.items():
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for attr in [
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"upper_bound",
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"lower_bound",
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"obj_coeff",
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"value",
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"reduced_cost",
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"sa_obj_up",
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"sa_obj_down",
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"sa_ub_up",
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"sa_ub_down",
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"sa_lb_up",
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"sa_lb_down",
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]:
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if getattr(c, attr) is not None:
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setattr(c, attr, round(getattr(c, attr), 6))
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if c.alvarez_2017 is not None:
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c.alvarez_2017 = list(np.round(c.alvarez_2017, 6))
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return vars
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def _round(obj: Any) -> Any:
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if isinstance(obj, tuple):
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if obj is None:
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return None
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return tuple([round(v, 6) for v in obj])
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if isinstance(obj, VariableFeatures):
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obj.reduced_costs = _round(obj.reduced_costs)
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obj.sa_obj_up = _round(obj.sa_obj_up)
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obj.sa_obj_down = _round(obj.sa_obj_down)
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obj.sa_lb_up = _round(obj.sa_lb_up)
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obj.sa_lb_down = _round(obj.sa_lb_down)
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obj.sa_ub_up = _round(obj.sa_ub_up)
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obj.sa_ub_down = _round(obj.sa_ub_down)
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obj.values = _round(obj.values)
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return obj
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def _filter_attrs(allowed_keys: List[str], obj: Any) -> Any:
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for key in obj.__dict__.keys():
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if key not in allowed_keys:
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setattr(obj, key, None)
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return obj
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def _remove_unsupported_constr_attrs(
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solver: InternalSolver,
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constraints: Dict[str, Constraint],
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) -> Dict[str, Constraint]:
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for (cname, c) in constraints.items():
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to_remove = []
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for k in c.__dict__.keys():
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if k not in solver.get_constraint_attrs():
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to_remove.append(k)
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for k in to_remove:
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setattr(c, k, None)
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return constraints
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def run_internal_solver_tests(solver: InternalSolver) -> None:
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run_basic_usage_tests(solver.clone())
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run_warm_start_tests(solver.clone())
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run_infeasibility_tests(solver.clone())
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run_iteration_cb_tests(solver.clone())
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if solver.are_callbacks_supported():
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run_lazy_cb_tests(solver.clone())
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def run_basic_usage_tests(solver: InternalSolver) -> None:
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# Create and set instance
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instance = solver.build_test_instance_knapsack()
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model = instance.to_model()
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solver.set_instance(instance, model)
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# Fetch variables (after-load)
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assert_equals(
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solver.get_variables(),
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VariableFeatures(
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names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
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lower_bounds=(0.0, 0.0, 0.0, 0.0, 0.0),
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upper_bounds=(1.0, 1.0, 1.0, 1.0, 67.0),
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types=("B", "B", "B", "B", "C"),
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obj_coeffs=(505.0, 352.0, 458.0, 220.0, 0.0),
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),
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)
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# Fetch constraints (after-load)
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assert_equals(
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_round_constraints(solver.get_constraints()),
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{
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"eq_capacity": Constraint(
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lazy=False,
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lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
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rhs=0.0,
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sense="=",
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)
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},
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)
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# Solve linear programming relaxation
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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_equals(round(lp_stats.lp_value, 3), 1287.923)
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assert lp_stats.lp_log is not None
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assert len(lp_stats.lp_log) > 100
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assert lp_stats.lp_wallclock_time is not None
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assert lp_stats.lp_wallclock_time > 0
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# Fetch variables (after-lp)
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assert_equals(
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_round(solver.get_variables(with_static=False)),
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_filter_attrs(
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solver.get_variable_attrs(),
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VariableFeatures(
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basis_status=("U", "B", "U", "L", "U"),
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reduced_costs=(193.615385, 0.0, 187.230769, -23.692308, 13.538462),
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sa_lb_down=(-inf, -inf, -inf, -0.111111, -inf),
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sa_lb_up=(1.0, 0.923077, 1.0, 1.0, 67.0),
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sa_obj_down=(311.384615, 317.777778, 270.769231, -inf, -13.538462),
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sa_obj_up=(inf, 570.869565, inf, 243.692308, inf),
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sa_ub_down=(0.913043, 0.923077, 0.9, 0.0, 43.0),
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sa_ub_up=(2.043478, inf, 2.2, inf, 69.0),
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values=(1.0, 0.923077, 1.0, 0.0, 67.0),
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),
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),
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)
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# Fetch constraints (after-lp)
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assert_equals(
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_round_constraints(solver.get_constraints()),
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_remove_unsupported_constr_attrs(
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solver,
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{
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"eq_capacity": Constraint(
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basis_status="N",
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dual_value=13.538462,
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lazy=False,
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lhs={
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"x[0]": 23.0,
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"x[1]": 26.0,
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"x[2]": 20.0,
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"x[3]": 18.0,
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"z": -1.0,
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},
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rhs=0.0,
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sa_rhs_down=-24.0,
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sa_rhs_up=1.9999999999999987,
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sense="=",
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slack=0.0,
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)
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},
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),
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)
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# Solve MIP
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mip_stats = solver.solve(
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tee=True,
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)
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assert not solver.is_infeasible()
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assert mip_stats.mip_log is not None
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assert len(mip_stats.mip_log) > 100
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assert mip_stats.mip_lower_bound is not None
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assert_equals(mip_stats.mip_lower_bound, 1183.0)
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assert mip_stats.mip_upper_bound is not None
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assert_equals(mip_stats.mip_upper_bound, 1183.0)
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assert mip_stats.mip_sense is not None
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assert_equals(mip_stats.mip_sense, "max")
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assert mip_stats.mip_wallclock_time is not None
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assert isinstance(mip_stats.mip_wallclock_time, float)
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assert mip_stats.mip_wallclock_time > 0
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# Fetch variables (after-mip)
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assert_equals(
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_round(solver.get_variables(with_static=False)),
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_filter_attrs(
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solver.get_variable_attrs(),
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VariableFeatures(values=(1.0, 0.0, 1.0, 1.0, 61.0)),
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),
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)
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# Fetch constraints (after-mip)
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assert_equals(
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_round_constraints(solver.get_constraints(with_static=False)),
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{"eq_capacity": Constraint(slack=0.0)},
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)
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# Build a new constraint
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cut = Constraint(lhs={"x[0]": 1.0}, sense="<", rhs=0.0)
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assert not solver.is_constraint_satisfied(cut)
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# Add new constraint and verify that it is listed. Modifying the model should
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# also clear the current solution.
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solver.add_constraint(cut, "cut")
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assert_equals(
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_round_constraints(solver.get_constraints()),
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{
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"eq_capacity": Constraint(
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lazy=False,
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lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
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rhs=0.0,
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sense="=",
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),
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"cut": Constraint(
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lazy=False,
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lhs={"x[0]": 1.0},
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rhs=0.0,
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sense="<",
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),
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},
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)
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# Re-solve MIP and verify that constraint affects the solution
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stats = solver.solve()
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assert_equals(stats.mip_lower_bound, 1030.0)
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assert solver.is_constraint_satisfied(cut)
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# Remove the new constraint
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solver.remove_constraint("cut")
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# New constraint should no longer affect solution
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stats = solver.solve()
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assert_equals(stats.mip_lower_bound, 1183.0)
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def run_warm_start_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_knapsack()
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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.mip_warm_start_value is not None:
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assert_equals(stats.mip_warm_start_value, 725.0)
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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.mip_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_equals(stats.mip_lower_bound, 725.0)
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assert_equals(stats.mip_upper_bound, 725.0)
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def run_infeasibility_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_infeasible()
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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.mip_upper_bound is None
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assert mip_stats.mip_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 run_iteration_cb_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_knapsack()
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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_equals(count, 5)
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def run_lazy_cb_tests(solver: InternalSolver) -> None:
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instance = solver.build_test_instance_knapsack()
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model = instance.to_model()
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def lazy_cb(cb_solver: InternalSolver, cb_model: Any) -> None:
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relsol = cb_solver.get_solution()
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assert relsol is not None
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assert relsol["x[0]"] is not None
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if relsol["x[0]"] > 0:
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instance.enforce_lazy_constraint(cb_solver, cb_model, "cut")
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solver.set_instance(instance, model)
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solver.solve(lazy_cb=lazy_cb)
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solution = solver.get_solution()
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assert solution is not None
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assert_equals(solution["x[0]"], 0.0)
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def assert_equals(left: Any, right: Any) -> None:
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assert left == right, f"left:\n{left}\nright:\n{right}"
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