MIPLearn/src/python/miplearn/solvers/learning.py

#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import logging
from copy import deepcopy
from typing import Optional, List

from p_tqdm import p_map

from .cplex import CPLEXSolver
from .gurobi import GurobiSolver
from .internal import InternalSolver
from .. import (ObjectiveValueComponent,
                PrimalSolutionComponent,
                LazyConstraintsComponent)

logger = logging.getLogger(__name__)


# Global memory for multiprocessing
SOLVER = [None]  # type: List[Optional[LearningSolver]]
INSTANCES = [None]  # type: List[Optional[dict]]


def _parallel_solve(instance_idx):
    solver = deepcopy(SOLVER[0])
    instance = INSTANCES[0][instance_idx]
    results = solver.solve(instance)
    return {
        "Results": results,
        "Solution": instance.solution,
        "LP solution": instance.lp_solution,
        "LP value": instance.lp_value,
        "Upper bound": instance.upper_bound,
        "Lower bound": instance.lower_bound,
        "Violations": instance.found_violations,
    }


class LearningSolver:
    """
    Mixed-Integer Linear Programming (MIP) solver that extracts information
    from previous runs, using Machine Learning methods, to accelerate the
    solution of new (yet unseen) instances.
    """

    def __init__(self,
                 components=None,
                 gap_tolerance=None,
                 mode="exact",
                 solver="gurobi",
                 threads=4,
                 time_limit=None):

        self.components = {}
        self.mode = mode
        self.internal_solver = None
        self.internal_solver_factory = solver
        self.threads = threads
        self.time_limit = time_limit
        self.gap_tolerance = gap_tolerance
        self.tee = False

        if components is not None:
            for comp in components:
                self.add(comp)
        else:
            self.add(ObjectiveValueComponent())
            self.add(PrimalSolutionComponent())
            self.add(LazyConstraintsComponent())

        assert self.mode in ["exact", "heuristic"]
        for component in self.components.values():
            component.mode = self.mode

    def _create_internal_solver(self):
        logger.debug("Initializing %s" % self.internal_solver_factory)
        if self.internal_solver_factory == "cplex":
            solver = CPLEXSolver()
        elif self.internal_solver_factory == "gurobi":
            solver = GurobiSolver()
        elif callable(self.internal_solver_factory):
            solver = self.internal_solver_factory()
            assert isinstance(solver, InternalSolver)
        else:
            raise Exception("solver %s not supported" % self.internal_solver_factory)
        solver.set_threads(self.threads)
        if self.time_limit is not None:
            solver.set_time_limit(self.time_limit)
        if self.gap_tolerance is not None:
            solver.set_gap_tolerance(self.gap_tolerance)
        return solver

    def solve(self,
              instance,
              model=None,
              tee=False,
              relaxation_only=False):

        if model is None:
            model = instance.to_model()

        self.tee = tee
        self.internal_solver = self._create_internal_solver()
        self.internal_solver.set_instance(instance, model=model)

        logger.debug("Solving LP relaxation...")
        results = self.internal_solver.solve_lp(tee=tee)
        instance.lp_solution = self.internal_solver.get_solution()
        instance.lp_value = results["Optimal value"]

        logger.debug("Running before_solve callbacks...")
        for component in self.components.values():
            component.before_solve(self, instance, model)

        if relaxation_only:
            return results

        results = self.internal_solver.solve(tee=tee)

        # Read MIP solution and bounds
        instance.lower_bound = results["Lower bound"]
        instance.upper_bound = results["Upper bound"]
        instance.solution = self.internal_solver.get_solution()

        logger.debug("Calling after_solve callbacks...")
        for component in self.components.values():
            component.after_solve(self, instance, model, results)

        return results

    def parallel_solve(self,
                       instances,
                       n_jobs=4,
                       label="Solve"):

        self.internal_solver = None
        SOLVER[0] = self
        INSTANCES[0] = instances
        p_map_results = p_map(_parallel_solve,
                              list(range(len(instances))),
                              num_cpus=n_jobs,
                              desc=label)

        results = [p["Results"] for p in p_map_results]
        for (idx, r) in enumerate(p_map_results):
            instances[idx].solution = r["Solution"]
            instances[idx].lp_solution = r["LP solution"]
            instances[idx].lp_value = r["LP value"]
            instances[idx].lower_bound = r["Lower bound"]
            instances[idx].upper_bound = r["Upper bound"]
            instances[idx].found_violations = r["Violations"]

        return results

    def fit(self, training_instances):
        if len(training_instances) == 0:
            return
        for component in self.components.values():
            component.fit(training_instances)

    def add(self, component):
        name = component.__class__.__name__
        self.components[name] = component

    def __getstate__(self):
        self.internal_solver = None
        return self.__dict__