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418 lines
15 KiB
418 lines
15 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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import traceback
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from typing import Optional, List, Any, cast, Callable, Dict, Tuple
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from p_tqdm import p_map
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from miplearn.components.component import Component
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from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
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from miplearn.components.dynamic_user_cuts import UserCutsComponent
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from miplearn.components.objective import ObjectiveValueComponent
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from miplearn.components.primal import PrimalSolutionComponent
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from miplearn.features import FeaturesExtractor, TrainingSample
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from miplearn.instance.base import Instance
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from miplearn.instance.picklegz import PickleGzInstance
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from miplearn.solvers import _RedirectOutput
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from miplearn.solvers.internal import InternalSolver
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from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
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from miplearn.types import LearningSolveStats
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logger = logging.getLogger(__name__)
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class _GlobalVariables:
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def __init__(self) -> None:
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self.solver: Optional[LearningSolver] = None
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self.instances: Optional[List[Instance]] = None
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self.discard_outputs: bool = False
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# Global variables used for multiprocessing. Global variables are copied by the
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# operating system when the process forks. Local variables are copied through
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# serialization, which is a much slower process.
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_GLOBAL = [_GlobalVariables()]
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def _parallel_solve(
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idx: int,
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) -> Tuple[Optional[LearningSolveStats], Optional[Instance]]:
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solver = _GLOBAL[0].solver
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instances = _GLOBAL[0].instances
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discard_outputs = _GLOBAL[0].discard_outputs
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assert solver is not None
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assert instances is not None
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try:
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stats = solver.solve(
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instances[idx],
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discard_output=discard_outputs,
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)
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return stats, instances[idx]
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except Exception as e:
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traceback.print_exc()
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logger.exception(f"Exception while solving {instances[idx]}. Ignoring.")
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return None, None
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class LearningSolver:
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"""
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Mixed-Integer Linear Programming (MIP) solver that extracts information
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from previous runs and uses Machine Learning methods to accelerate the
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solution of new (yet unseen) instances.
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Parameters
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----------
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components: List[Component]
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Set of components in the solver. By default, includes
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`ObjectiveValueComponent`, `PrimalSolutionComponent`,
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`DynamicLazyConstraintsComponent` and `UserCutsComponent`.
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mode: str
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If "exact", solves problem to optimality, keeping all optimality
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guarantees provided by the MIP solver. If "heuristic", uses machine
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learning more aggressively, and may return suboptimal solutions.
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solver: Callable[[], InternalSolver]
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A callable that constructs the internal solver. If None is provided,
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use GurobiPyomoSolver.
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use_lazy_cb: bool
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If true, use native solver callbacks for enforcing lazy constraints,
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instead of a simple loop. May not be supported by all solvers.
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solve_lp: bool
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If true, solve the root LP relaxation before solving the MIP. This
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option should be activated if the LP relaxation is not very
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expensive to solve and if it provides good hints for the integer
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solution.
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simulate_perfect: bool
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If true, each call to solve actually performs three actions: solve
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the original problem, train the ML models on the data that was just
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collected, and solve the problem again. This is useful for evaluating
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the theoretical performance of perfect ML models.
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"""
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def __init__(
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self,
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components: Optional[List[Component]] = None,
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mode: str = "exact",
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solver: Optional[InternalSolver] = None,
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use_lazy_cb: bool = False,
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solve_lp: bool = True,
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simulate_perfect: bool = False,
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) -> None:
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if solver is None:
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solver = GurobiPyomoSolver()
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assert isinstance(solver, InternalSolver)
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self.components: Dict[str, Component] = {}
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self.internal_solver: Optional[InternalSolver] = None
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self.internal_solver_prototype: InternalSolver = solver
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self.mode: str = mode
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self.simulate_perfect: bool = simulate_perfect
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self.solve_lp: bool = solve_lp
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self.tee = False
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self.use_lazy_cb: bool = use_lazy_cb
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if components is not None:
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for comp in components:
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self._add_component(comp)
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else:
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self._add_component(ObjectiveValueComponent())
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self._add_component(PrimalSolutionComponent(mode=mode))
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self._add_component(DynamicLazyConstraintsComponent())
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self._add_component(UserCutsComponent())
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assert self.mode in ["exact", "heuristic"]
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def _solve(
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self,
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instance: Instance,
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model: Any = None,
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discard_output: bool = False,
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tee: bool = False,
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) -> LearningSolveStats:
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# Generate model
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# -------------------------------------------------------
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instance.load()
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if model is None:
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with _RedirectOutput([]):
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model = instance.to_model()
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# Initialize training sample
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# -------------------------------------------------------
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training_sample = TrainingSample()
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instance.training_data += [training_sample]
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# Initialize stats
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# -------------------------------------------------------
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stats: LearningSolveStats = {}
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# Initialize internal solver
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# -------------------------------------------------------
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self.tee = tee
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self.internal_solver = self.internal_solver_prototype.clone()
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assert self.internal_solver is not None
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assert isinstance(self.internal_solver, InternalSolver)
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self.internal_solver.set_instance(instance, model)
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# Extract features
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# -------------------------------------------------------
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logger.info("Extracting features...")
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FeaturesExtractor(self.internal_solver).extract(instance)
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callback_args = (
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self,
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instance,
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model,
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stats,
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instance.features,
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training_sample,
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)
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# Solve root LP relaxation
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# -------------------------------------------------------
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if self.solve_lp:
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logger.debug("Running before_solve_lp callbacks...")
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for component in self.components.values():
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component.before_solve_lp(*callback_args)
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logger.info("Solving root LP relaxation...")
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lp_stats = self.internal_solver.solve_lp(tee=tee)
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stats.update(cast(LearningSolveStats, lp_stats))
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training_sample.lp_solution = self.internal_solver.get_solution()
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training_sample.lp_value = lp_stats["LP value"]
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training_sample.lp_log = lp_stats["LP log"]
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logger.debug("Running after_solve_lp callbacks...")
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for component in self.components.values():
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component.after_solve_lp(*callback_args)
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# Callback wrappers
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# -------------------------------------------------------
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def iteration_cb_wrapper() -> bool:
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should_repeat = False
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for comp in self.components.values():
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if comp.iteration_cb(self, instance, model):
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should_repeat = True
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return should_repeat
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def lazy_cb_wrapper(
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cb_solver: InternalSolver,
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cb_model: Any,
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) -> None:
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for comp in self.components.values():
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comp.lazy_cb(self, instance, model)
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def user_cut_cb_wrapper(
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cb_solver: InternalSolver,
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cb_model: Any,
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) -> None:
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for comp in self.components.values():
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comp.user_cut_cb(self, instance, model)
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lazy_cb = None
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if self.use_lazy_cb:
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lazy_cb = lazy_cb_wrapper
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user_cut_cb = None
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if instance.has_user_cuts():
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user_cut_cb = user_cut_cb_wrapper
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# Before-solve callbacks
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# -------------------------------------------------------
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logger.debug("Running before_solve_mip callbacks...")
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for component in self.components.values():
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component.before_solve_mip(*callback_args)
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# Solve MIP
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# -------------------------------------------------------
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logger.info("Solving MIP...")
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mip_stats = self.internal_solver.solve(
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tee=tee,
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iteration_cb=iteration_cb_wrapper,
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user_cut_cb=user_cut_cb,
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lazy_cb=lazy_cb,
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)
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stats.update(cast(LearningSolveStats, mip_stats))
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if training_sample.lp_value is not None:
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stats["LP value"] = training_sample.lp_value
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stats["Solver"] = "default"
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stats["Gap"] = self._compute_gap(
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ub=stats["Upper bound"],
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lb=stats["Lower bound"],
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)
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stats["Mode"] = self.mode
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# Add some information to training_sample
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# -------------------------------------------------------
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training_sample.lower_bound = stats["Lower bound"]
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training_sample.upper_bound = stats["Upper bound"]
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training_sample.mip_log = stats["MIP log"]
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training_sample.solution = self.internal_solver.get_solution()
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# After-solve callbacks
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# -------------------------------------------------------
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logger.debug("Calling after_solve_mip callbacks...")
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for component in self.components.values():
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component.after_solve_mip(*callback_args)
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# Flush
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# -------------------------------------------------------
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if not discard_output:
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instance.flush()
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return stats
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def solve(
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self,
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instance: Instance,
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model: Any = None,
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discard_output: bool = False,
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tee: bool = False,
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) -> LearningSolveStats:
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"""
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Solves the given instance. If trained machine-learning models are
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available, they will be used to accelerate the solution process.
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The argument `instance` may be either an Instance object or a
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filename pointing to a pickled Instance object.
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This method adds a new training sample to `instance.training_sample`.
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If a filename is provided, then the file is modified in-place. That is,
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the original file is overwritten.
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If `solver.solve_lp_first` is False, the properties lp_solution and
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lp_value will be set to dummy values.
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Parameters
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----------
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instance: Instance
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The instance to be solved.
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model: Any
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The corresponding Pyomo model. If not provided, it will be created.
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discard_output: bool
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If True, do not write the modified instances anywhere; simply discard
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them. Useful during benchmarking.
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tee: bool
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If true, prints solver log to screen.
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Returns
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-------
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LearningSolveStats
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A dictionary of solver statistics containing at least the following
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keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes",
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"Sense", "Log", "Warm start value" and "LP value".
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Additional components may generate additional keys. For example,
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ObjectiveValueComponent adds the keys "Predicted LB" and
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"Predicted UB". See the documentation of each component for more
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details.
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"""
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if self.simulate_perfect:
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if not isinstance(instance, PickleGzInstance):
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raise Exception("Not implemented")
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self._solve(
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instance=instance,
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model=model,
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tee=tee,
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discard_output=True,
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)
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self.fit([instance])
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instance.instance = None
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return self._solve(
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instance=instance,
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model=model,
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discard_output=discard_output,
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tee=tee,
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)
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def parallel_solve(
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self,
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instances: List[Instance],
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n_jobs: int = 4,
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label: str = "Solve",
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discard_outputs: bool = False,
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) -> List[LearningSolveStats]:
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"""
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Solves multiple instances in parallel.
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This method is equivalent to calling `solve` for each item on the list,
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but it processes multiple instances at the same time. Like `solve`, this
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method modifies each instance in place. Also like `solve`, a list of
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filenames may be provided.
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Parameters
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----------
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discard_outputs: bool
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If True, do not write the modified instances anywhere; simply discard
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them instead. Useful during benchmarking.
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label: str
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Label to show in the progress bar.
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instances: List[Instance]
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The instances to be solved.
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n_jobs: int
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Number of instances to solve in parallel at a time.
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Returns
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-------
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List[LearningSolveStats]
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List of solver statistics, with one entry for each provided instance.
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The list is the same you would obtain by calling
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`[solver.solve(p) for p in instances]`
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"""
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if n_jobs == 1:
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return [self.solve(p) for p in instances]
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else:
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self.internal_solver = None
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self._silence_miplearn_logger()
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_GLOBAL[0].solver = self
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_GLOBAL[0].instances = instances
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_GLOBAL[0].discard_outputs = discard_outputs
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results = p_map(
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_parallel_solve,
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list(range(len(instances))),
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num_cpus=n_jobs,
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desc=label,
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)
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results = [r for r in results if r[0]]
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stats = []
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for (idx, (s, instance)) in enumerate(results):
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stats.append(s)
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instances[idx] = instance
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self._restore_miplearn_logger()
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return stats
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def fit(self, training_instances: List[Instance]) -> None:
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logger.debug("Fitting...")
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if len(training_instances) == 0:
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return
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for component in self.components.values():
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component.fit(training_instances)
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def _add_component(self, component: Component) -> None:
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name = component.__class__.__name__
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self.components[name] = component
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def _silence_miplearn_logger(self) -> None:
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miplearn_logger = logging.getLogger("miplearn")
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self.prev_log_level = miplearn_logger.getEffectiveLevel()
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miplearn_logger.setLevel(logging.WARNING)
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def _restore_miplearn_logger(self) -> None:
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miplearn_logger = logging.getLogger("miplearn")
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miplearn_logger.setLevel(self.prev_log_level)
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def __getstate__(self) -> Dict:
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self.internal_solver = None
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return self.__dict__
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@staticmethod
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def _compute_gap(ub: Optional[float], lb: Optional[float]) -> Optional[float]:
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if lb is None or ub is None or lb * ub < 0:
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# solver did not find a solution and/or bound
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return None
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elif abs(ub - lb) < 1e-6:
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# avoid division by zero when ub = lb = 0
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return 0.0
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else:
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# divide by max(abs(ub),abs(lb)) to ensure gap <= 1
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return (ub - lb) / max(abs(ub), abs(lb))
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