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358 lines
13 KiB
358 lines
13 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 ObjectiveValueComponent, PrimalSolutionComponent, LazyConstraintsComponent
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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 numpy as np
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import logging
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logger = logging.getLogger(__name__)
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# Global memory for multiprocessing
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SOLVER = [None]
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INSTANCES = [None]
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def _parallel_solve(instance_idx):
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solver = deepcopy(SOLVER[0])
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instance = INSTANCES[0][instance_idx]
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results = solver.solve(instance)
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return {
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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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"Violations": instance.found_violations,
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}
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class InternalSolver:
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def __init__(self):
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self.is_warm_start_available = False
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self.model = None
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self.var_name_to_var = {}
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def solve_lp(self, tee=False):
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self.solver.set_instance(self.model)
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# Relax domain
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from pyomo.core.base.set_types import Reals, Binary
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original_domains = []
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for (idx, var) in enumerate(self.model.component_data_objects(Var)):
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original_domains += [var.domain]
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lb, ub = var.bounds
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if var.domain == Binary:
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var.domain = Reals
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var.setlb(lb)
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var.setub(ub)
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self.solver.update_var(var)
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# Solve LP relaxation
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results = self.solver.solve(tee=tee)
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# Restore domains
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for (idx, var) in enumerate(self.model.component_data_objects(Var)):
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if original_domains[idx] == Binary:
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var.domain = original_domains[idx]
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self.solver.update_var(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):
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for var in self.model.component_objects(Var):
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for index in var:
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if var[index].fixed:
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continue
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var[index].value = None
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def get_solution(self):
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solution = {}
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for var in self.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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def set_warm_start(self, solution):
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self.is_warm_start_available = True
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self.clear_values()
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count_total, count_fixed = 0, 0
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for var_name in solution:
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var = self.var_name_to_var[var_name]
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for index in solution[var_name]:
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count_total += 1
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var[index].value = solution[var_name][index]
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if solution[var_name][index] is not None:
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count_fixed += 1
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logger.info("Setting start values for %d variables (out of %d)" %
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(count_fixed, count_total))
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def set_model(self, model):
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from pyomo.core.kernel.objective import minimize, maximize
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self.model = model
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self.solver.set_instance(model)
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if self.solver._objective.sense == minimize:
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self.sense = "min"
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else:
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self.sense = "max"
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self.var_name_to_var = {}
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for var in model.component_objects(Var):
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self.var_name_to_var[var.name] = var
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def set_instance(self, instance):
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self.instance = instance
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def fix(self, solution):
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count_total, count_fixed = 0, 0
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for var_name in solution:
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for index in solution[var_name]:
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var = self.var_name_to_var[var_name]
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count_total += 1
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if solution[var_name][index] is None:
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continue
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count_fixed += 1
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var[index].fix(solution[var_name][index])
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self.solver.update_var(var[index])
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logger.info("Fixing values for %d variables (out of %d)" %
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(count_fixed, count_total))
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def solve(self, tee=False):
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total_wallclock_time = 0
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self.instance.found_violations = []
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while True:
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logger.debug("Solving MIP...")
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results = self.solver.solve(tee=tee)
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total_wallclock_time += results["Solver"][0]["Wallclock time"]
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if not hasattr(self.instance, "find_violations"):
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break
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logger.debug("Finding violated constraints...")
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violations = self.instance.find_violations(self.model)
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if len(violations) == 0:
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break
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self.instance.found_violations += violations
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logger.debug(" %d violations found" % len(violations))
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for v in violations:
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cut = self.instance.build_lazy_constraint(self.model, v)
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self.solver.add_constraint(cut)
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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": total_wallclock_time,
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"Nodes": 1,
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"Sense": self.sense,
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}
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class GurobiSolver(InternalSolver):
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def __init__(self):
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super().__init__()
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self.solver = pe.SolverFactory('gurobi_persistent')
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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, tee=False):
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from gurobipy import GRB
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def cb(cb_model, cb_opt, cb_where):
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if cb_where == GRB.Callback.MIPSOL:
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all_vars = [v[idx] for v in self.model.component_objects(Var) for idx in v]
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cb_opt.cbGetSolution(all_vars)
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logger.debug("Finding violated constraints...")
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violations = self.instance.find_violations(cb_model)
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self.instance.found_violations += violations
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logger.debug(" %d violations found" % len(violations))
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for v in violations:
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cut = self.instance.build_lazy_constraint(cb_model, v)
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cb_opt.cbLazy(cut)
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if hasattr(self.instance, "find_violations"):
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self.solver.options["LazyConstraints"] = 1
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self.solver.set_callback(cb)
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self.instance.found_violations = []
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results = self.solver.solve(tee=tee, warmstart=self.is_warm_start_available)
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self.solver.set_callback(None)
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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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"Sense": self.sense,
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}
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class CPLEXSolver(InternalSolver):
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def __init__(self):
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super().__init__()
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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_lp(self, tee=False):
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import cplex
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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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"ObjectiveValue": ObjectiveValueComponent(),
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"PrimalSolution": PrimalSolutionComponent(),
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"LazyConstraints": LazyConstraintsComponent(),
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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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logger.debug("Initializing %s" % self.internal_solver_factory)
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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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self.internal_solver.set_model(model)
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self.internal_solver.set_instance(instance)
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logger.debug("Solving LP relaxation...")
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results = self.internal_solver.solve_lp(tee=tee)
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instance.lp_solution = self.internal_solver.get_solution()
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instance.lp_value = results["Optimal value"]
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logger.debug("Running 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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results = self.internal_solver.solve(tee=tee)
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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()
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logger.debug("Calling after_solve callbacks...")
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for component in self.components.values():
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component.after_solve(self, instance, model, results)
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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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SOLVER[0] = self
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INSTANCES[0] = instances
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p_map_results = p_map(_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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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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instances[idx].found_violations = r["Violations"]
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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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