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299 lines
11 KiB
299 lines
11 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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from . import WarmStartComponent, BranchPriorityComponent, ObjectiveValueComponent
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import pyomo.environ as pe
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from pyomo.core import Var
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from copy import deepcopy
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import pickle
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from scipy.stats import randint
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from p_tqdm import p_map
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import logging
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logger = logging.getLogger(__name__)
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class InternalSolver():
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def __init__():
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pass
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def solve_lp(self, model, tee=False):
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from pyomo.core.base.set_types import Reals
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original_domain = {}
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for var in model.component_data_objects(Var):
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original_domain[str(var)] = var.domain
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lb, ub = var.bounds
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var.setlb(lb)
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var.setub(ub)
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var.domain = Reals
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self.solver.set_instance(model)
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results = self.solver.solve(tee=True)
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for var in model.component_data_objects(Var):
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var.domain = original_domain[str(var)]
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return {
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"Optimal value": results["Problem"][0]["Lower bound"],
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}
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def clear_values(self, model):
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for var in model.component_objects(Var):
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for index in var:
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var[index].value = None
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def get_solution(self, model):
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solution = {}
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for var in model.component_objects(Var):
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solution[str(var)] = {}
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for index in var:
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solution[str(var)][index] = var[index].value
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return solution
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class GurobiSolver(InternalSolver):
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def __init__(self):
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self.solver = pe.SolverFactory('gurobi_persistent')
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#self.solver.options["OutputFlag"] = 0
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self.solver.options["Seed"] = randint(low=0, high=1000).rvs()
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def set_threads(self, threads):
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self.solver.options["Threads"] = threads
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def set_time_limit(self, time_limit):
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self.solver.options["TimeLimit"] = time_limit
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def set_gap_tolerance(self, gap_tolerance):
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self.solver.options["MIPGap"] = gap_tolerance
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def solve(self, model, tee=False, warmstart=False):
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self.solver.set_instance(model)
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results = self.solver.solve(tee=tee, warmstart=warmstart)
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return {
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"Lower bound": results["Problem"][0]["Lower bound"],
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"Upper bound": results["Problem"][0]["Upper bound"],
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"Wallclock time": results["Solver"][0]["Wallclock time"],
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"Nodes": self.solver._solver_model.getAttr("NodeCount"),
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}
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def _load_vars(self):
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var_map = self._pyomo_var_to_solver_var_map
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ref_vars = self._referenced_variables
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vars_to_load = var_map.keys()
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gurobi_vars_to_load = [var_map[pyomo_var] for pyomo_var in vars_to_load]
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vals = self._solver_model.getAttr("X", gurobi_vars_to_load)
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for var, val in zip(vars_to_load, vals):
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if ref_vars[var] > 0:
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var.stale = False
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var.value = val
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class CPLEXSolver(InternalSolver):
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def __init__(self):
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import cplex
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self.solver = pe.SolverFactory('cplex_persistent')
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self.solver.options["randomseed"] = randint(low=0, high=1000).rvs()
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def set_threads(self, threads):
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self.solver.options["threads"] = threads
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def set_time_limit(self, time_limit):
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self.solver.options["timelimit"] = time_limit
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def set_gap_tolerance(self, gap_tolerance):
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self.solver.options["mip_tolerances_mipgap"] = gap_tolerance
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def solve(self, model, tee=False, warmstart=False):
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self.solver.set_instance(model)
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results = self.solver.solve(tee=tee, warmstart=warmstart)
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return {
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"Lower bound": results["Problem"][0]["Lower bound"],
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"Upper bound": results["Problem"][0]["Upper bound"],
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"Wallclock time": results["Solver"][0]["Wallclock time"],
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"Nodes": 1,
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}
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def solve_lp(self, model, tee=False):
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import cplex
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self.solver.set_instance(model)
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lp = self.solver._solver_model
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var_types = lp.variables.get_types()
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n_vars = len(var_types)
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lp.set_problem_type(cplex.Cplex.problem_type.LP)
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results = self.solver.solve(tee=tee)
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lp.variables.set_types(zip(range(n_vars), var_types))
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return {
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"Optimal value": results["Problem"][0]["Lower bound"],
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}
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class LearningSolver:
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"""
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Mixed-Integer Linear Programming (MIP) solver that extracts information from previous runs,
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using Machine Learning methods, to accelerate the solution of new (yet unseen) instances.
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"""
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def __init__(self,
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components=None,
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gap_tolerance=None,
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mode="exact",
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solver="gurobi",
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threads=4,
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time_limit=None,
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):
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self.is_persistent = None
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self.components = components
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self.mode = mode
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self.internal_solver = None
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self.internal_solver_factory = solver
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self.threads = threads
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self.time_limit = time_limit
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self.gap_tolerance = gap_tolerance
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self.tee = False
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self.training_instances = []
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if self.components is not None:
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assert isinstance(self.components, dict)
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else:
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self.components = {
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"obj-val": ObjectiveValueComponent(),
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#"warm-start": WarmStartComponent(),
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}
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assert self.mode in ["exact", "heuristic"]
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for component in self.components.values():
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component.mode = self.mode
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def _create_internal_solver(self):
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if self.internal_solver_factory == "cplex":
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solver = CPLEXSolver()
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elif self.internal_solver_factory == "gurobi":
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solver = GurobiSolver()
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else:
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raise Exception("solver %s not supported" % solver_factory)
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solver.set_threads(self.threads)
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if self.time_limit is not None:
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solver.set_time_limit(self.time_limit)
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if self.gap_tolerance is not None:
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solver.set_gap_tolerance(self.gap_tolerance)
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return solver
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def solve(self,
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instance,
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model=None,
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tee=False,
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relaxation_only=False,
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):
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if model is None:
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model = instance.to_model()
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self.tee = tee
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self.internal_solver = self._create_internal_solver()
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# Solve LP relaxation
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results = self.internal_solver.solve_lp(model, tee=tee)
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instance.lp_solution = self.internal_solver.get_solution(model)
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instance.lp_value = results["Optimal value"]
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# Invoke before_solve callbacks
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for component in self.components.values():
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component.before_solve(self, instance, model)
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if relaxation_only:
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return results
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# Check if warm start is available
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is_warm_start_available = False
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if "warm-start" in self.components.keys():
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if self.components["warm-start"].is_warm_start_available:
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is_warm_start_available = True
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# Solver original MIP
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self.internal_solver.clear_values(model)
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results = self.internal_solver.solve(model,
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tee=tee,
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warmstart=is_warm_start_available)
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# Read MIP solution and bounds
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instance.lower_bound = results["Lower bound"]
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instance.upper_bound = results["Upper bound"]
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instance.solution = self.internal_solver.get_solution(model)
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# Invoke after_solve callbacks
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for component in self.components.values():
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component.after_solve(self, instance, model)
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# Store instance for future training
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self.training_instances += [instance]
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return results
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def parallel_solve(self,
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instances,
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n_jobs=4,
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label="Solve",
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collect_training_data=True,
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):
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self.internal_solver = None
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def _process(instance):
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solver = deepcopy(self)
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results = solver.solve(instance)
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solver.internal_solver = None
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if not collect_training_data:
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solver.components = {}
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return {
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"Solver": solver,
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"Results": results,
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"Solution": instance.solution,
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"LP solution": instance.lp_solution,
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"LP value": instance.lp_value,
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"Upper bound": instance.upper_bound,
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"Lower bound": instance.lower_bound,
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}
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p_map_results = p_map(_process, instances, num_cpus=n_jobs, desc=label)
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subsolvers = [p["Solver"] for p in p_map_results]
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results = [p["Results"] for p in p_map_results]
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for (idx, r) in enumerate(p_map_results):
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instances[idx].solution = r["Solution"]
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instances[idx].lp_solution = r["LP solution"]
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instances[idx].lp_value = r["LP value"]
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instances[idx].lower_bound = r["Lower bound"]
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instances[idx].upper_bound = r["Upper bound"]
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for (name, component) in self.components.items():
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subcomponents = [subsolver.components[name]
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for subsolver in subsolvers
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if name in subsolver.components.keys()]
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self.components[name].merge(subcomponents)
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return results
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def fit(self, training_instances=None):
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if training_instances is None:
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training_instances = self.training_instances
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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 save_state(self, filename):
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with open(filename, "wb") as file:
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pickle.dump({
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"version": 2,
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"components": self.components,
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}, file)
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def load_state(self, filename):
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with open(filename, "rb") as file:
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data = pickle.load(file)
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assert data["version"] == 2
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for (component_name, component) in data["components"].items():
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if component_name not in self.components.keys():
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continue
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else:
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self.components[component_name].merge([component])
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