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# 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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import logging
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import re
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import sys
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from abc import ABC, abstractmethod
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from copy import deepcopy
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from io import StringIO
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from typing import Optional, List
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import pyomo.core.kernel.objective
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import pyomo.environ as pe
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from p_tqdm import p_map
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from pyomo.core import Var
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from scipy.stats import randint
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from . import (ObjectiveValueComponent,
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PrimalSolutionComponent,
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LazyConstraintsComponent)
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from .instance import Instance
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logger = logging.getLogger(__name__)
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# Global memory for multiprocessing
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SOLVER = [None] # type: List[Optional[LearningSolver]]
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INSTANCES = [None] # type: List[Optional[dict]]
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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 RedirectOutput(object):
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def __init__(self, streams):
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self.streams = streams
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self._original_stdout = sys.stdout
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self._original_stderr = sys.stderr
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sys.stdout = self
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sys.stderr = self
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def __del__(self):
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sys.stdout = self._original_stdout
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sys.stderr = self._original_stderr
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def write(self, data):
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for stream in self.streams:
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stream.write(data)
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def flush(self):
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for stream in self.streams:
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stream.flush()
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def __enter__(self):
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pass
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def __exit__(self, _type, _value, _traceback):
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pass
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class InternalSolver(ABC):
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"""
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The MIP solver used internaly by LearningSolver.
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Attributes
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----------
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instance: miplearn.Instance
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The MIPLearn instance currently loaded to the solver
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model: pyomo.core.ConcreteModel
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The Pyomo model currently loaded on the solver
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"""
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def __init__(self):
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self.instance = None
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self.model = None
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self._all_vars = None
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self._bin_vars = None
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self._is_warm_start_available = False
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self._pyomo_solver = None
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self._obj_sense = None
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self._varname_to_var = {}
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def solve_lp(self, tee=False):
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"""
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Solves the LP relaxation of the currently loaded instance.
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Parameters
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----------
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tee: bool
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If true, prints the solver log to the screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing the following keys:
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"Optimal value".
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"""
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for var in self._bin_vars:
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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 = pyomo.core.base.set_types.Reals
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self._pyomo_solver.update_var(var)
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results = self._pyomo_solver.solve(tee=tee)
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for var in self._bin_vars:
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var.domain = pyomo.core.base.set_types.Binary
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self._pyomo_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 get_solution(self):
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"""
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Returns current solution found by the solver.
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If called after `solve`, returns the best primal solution found during
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the search. If called after `solve_lp`, returns the optimal solution
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to the LP relaxation.
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The solution is a dictionary `sol`, where the optimal value of `var[idx]`
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is given by `sol[var][idx]`.
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"""
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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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"""
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Sets the warm start to be used by the solver.
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The solution should be a dictionary following the same format as the
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one produced by `get_solution`. Only one warm start is currently
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supported. Calling this function when a warm start already exists will
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remove the previous warm start.
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"""
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self.clear_warm_start()
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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._varname_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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if count_fixed > 0:
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self._is_warm_start_available = True
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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 clear_warm_start(self):
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"""
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Removes any existing warm start from the solver.
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"""
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for var in self._all_vars:
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if not var.fixed:
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var.value = None
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self._is_warm_start_available = False
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def set_instance(self, instance, model=None):
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"""
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Loads the given instance into the solver.
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Parameters
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----------
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instance: miplearn.Instance
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The instance to be loaded.
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model: pyomo.core.ConcreteModel
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The corresponding Pyomo model. If not provided, it will be
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generated by calling `instance.to_model()`.
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"""
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if model is None:
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model = instance.to_model()
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assert isinstance(instance, Instance)
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assert isinstance(model, pe.ConcreteModel)
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self.instance = instance
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self.model = model
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self._pyomo_solver.set_instance(model)
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# Update objective sense
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self._obj_sense = "max"
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if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
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self._obj_sense = "min"
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# Update variables
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self._all_vars = []
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self._bin_vars = []
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self._varname_to_var = {}
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for var in model.component_objects(Var):
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self._varname_to_var[var.name] = var
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for idx in var:
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self._all_vars += [var[idx]]
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if var[idx].domain == pyomo.core.base.set_types.Binary:
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self._bin_vars += [var[idx]]
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def fix(self, solution):
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"""
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Fixes the values of a subset of decision variables.
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The values should be provided in the dictionary format generated by
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`get_solution`. Missing values in the solution indicate variables
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that should be left free.
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"""
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count_total, count_fixed = 0, 0
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for varname in solution:
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for index in solution[varname]:
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var = self._varname_to_var[varname]
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count_total += 1
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if solution[varname][index] is None:
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continue
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count_fixed += 1
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var[index].fix(solution[varname][index])
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self._pyomo_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 add_constraint(self, constraint):
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"""
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Adds a single constraint to the model.
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"""
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self._pyomo_solver.add_constraint(constraint)
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def solve(self, tee=False):
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"""
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Solves the currently loaded instance.
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Parameters
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----------
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tee: bool
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If true, prints the solver log to the screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing the following keys:
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"Lower bound", "Upper bound", "Wallclock time", "Nodes", "Sense",
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"Log" and "Warm start value".
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"""
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total_wallclock_time = 0
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streams = [StringIO()]
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if tee:
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streams += [sys.stdout]
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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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with RedirectOutput(streams):
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results = self._pyomo_solver.solve(tee=True,
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warmstart=self._is_warm_start_available)
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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.add_constraint(cut)
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log = streams[0].getvalue()
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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._obj_sense,
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"Log": log,
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"Warm start value": self.extract_warm_start_value(log),
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}
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def extract_warm_start_value(self, log):
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"""
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Extracts and returns the objective value of the user-provided MIP start
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from the provided solver log. If more than one value is found, returns
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the last one. If no value is present in the logs, returns None.
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"""
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ws_value = None
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for line in log.splitlines():
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matches = re.findall(self._get_warm_start_regexp(), line)
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if len(matches) == 0:
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continue
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ws_value = float(matches[0])
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return ws_value
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def set_threads(self, threads):
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key = self._get_threads_option_name()
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self._pyomo_solver.options[key] = threads
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def set_time_limit(self, time_limit):
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key = self._get_time_limit_option_name()
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self._pyomo_solver.options[key] = time_limit
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def set_gap_tolerance(self, gap_tolerance):
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key = self._get_gap_tolerance_option_name()
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self._pyomo_solver.options[key] = gap_tolerance
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@abstractmethod
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def _get_warm_start_regexp(self):
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pass
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@abstractmethod
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def _get_threads_option_name(self):
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pass
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@abstractmethod
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def _get_time_limit_option_name(self):
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pass
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@abstractmethod
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def _get_gap_tolerance_option_name(self):
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pass
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class GurobiSolver(InternalSolver):
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def __init__(self,
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use_lazy_callbacks=False,
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options=None):
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"""
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Creates a new GurobiSolver.
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Parameters
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----------
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use_lazy_callbacks: bool
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If true, lazy constraints will be enforced via lazy callbacks.
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Otherwise, they will be enforced via a simple solve-check loop.
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options: dict
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Dictionary of options to pass to the Pyomo solver. For example,
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{"Threads": 4} to set the number of threads.
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"""
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super().__init__()
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self._use_lazy_callbacks = use_lazy_callbacks
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self._pyomo_solver = pe.SolverFactory('gurobi_persistent')
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self._pyomo_solver.options["Seed"] = randint(low=0, high=1000).rvs()
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if options is not None:
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for (key, value) in options.items():
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self._pyomo_solver.options[key] = value
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def solve(self, tee=False):
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if self._use_lazy_callbacks:
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return self._solve_with_callbacks(tee)
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else:
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return super().solve(tee)
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def _solve_with_callbacks(self, tee):
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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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cb_opt.cbGetSolution(self._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._pyomo_solver.options["LazyConstraints"] = 1
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self._pyomo_solver.set_callback(cb)
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self.instance.found_violations = []
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print(self._is_warm_start_available)
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streams = [StringIO()]
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if tee:
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streams += [sys.stdout]
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with RedirectOutput(streams):
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results = self._pyomo_solver.solve(tee=True,
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warmstart=self._is_warm_start_available)
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self._pyomo_solver.set_callback(None)
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node_count = int(self._pyomo_solver._solver_model.getAttr("NodeCount"))
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log = streams[0].getvalue()
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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": max(1, node_count),
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"Sense": self._obj_sense,
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"Log": log,
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"Warm start value": self.extract_warm_start_value(log),
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}
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def _get_warm_start_regexp(self):
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return "MIP start with objective ([0-9.e+-]*)"
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def _get_threads_option_name(self):
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return "Threads"
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def _get_time_limit_option_name(self):
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return "TimeLimit"
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def _get_gap_tolerance_option_name(self):
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return "MIPGap"
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class CPLEXSolver(InternalSolver):
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def __init__(self, options=None):
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"""
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Creates a new CPLEXSolver.
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Parameters
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----------
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options: dict
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Dictionary of options to pass to the Pyomo solver. For example,
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{"mip_display": 5} to increase the log verbosity.
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"""
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super().__init__()
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self._pyomo_solver = pe.SolverFactory('cplex_persistent')
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self._pyomo_solver.options["randomseed"] = randint(low=0, high=1000).rvs()
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self._pyomo_solver.options["mip_display"] = 4
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if options is not None:
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for (key, value) in options.items():
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self._pyomo_solver.options[key] = value
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def set_threads(self, threads):
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self._pyomo_solver.options["threads"] = threads
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def set_time_limit(self, time_limit):
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self._pyomo_solver.options["timelimit"] = time_limit
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def set_gap_tolerance(self, gap_tolerance):
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self._pyomo_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._pyomo_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._pyomo_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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def _get_warm_start_regexp(self):
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return "MIP start .* with objective ([0-9.e+-]*)\\."
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def _get_threads_option_name(self):
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return "threads"
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def _get_time_limit_option_name(self):
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return "timelimit"
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def _get_gap_tolerance_option_name(self):
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return "mip_gap_tolerances_mipgap"
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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, using Machine Learning methods, to accelerate the
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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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self.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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if components is not None:
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for comp in components:
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self.add(comp)
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else:
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self.add(ObjectiveValueComponent())
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self.add(PrimalSolutionComponent())
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self.add(LazyConstraintsComponent())
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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):
|
||||
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__
|
||||
@ -0,0 +1,32 @@
|
||||
# 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 sys
|
||||
|
||||
|
||||
class RedirectOutput(object):
|
||||
def __init__(self, streams):
|
||||
self.streams = streams
|
||||
self._original_stdout = sys.stdout
|
||||
self._original_stderr = sys.stderr
|
||||
sys.stdout = self
|
||||
sys.stderr = self
|
||||
|
||||
def __del__(self):
|
||||
sys.stdout = self._original_stdout
|
||||
sys.stderr = self._original_stderr
|
||||
|
||||
def write(self, data):
|
||||
for stream in self.streams:
|
||||
stream.write(data)
|
||||
|
||||
def flush(self):
|
||||
for stream in self.streams:
|
||||
stream.flush()
|
||||
|
||||
def __enter__(self):
|
||||
pass
|
||||
|
||||
def __exit__(self, _type, _value, _traceback):
|
||||
pass
|
||||
@ -0,0 +1,61 @@
|
||||
# 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 pyomo.environ as pe
|
||||
from scipy.stats import randint
|
||||
|
||||
from .internal import InternalSolver
|
||||
|
||||
|
||||
class CPLEXSolver(InternalSolver):
|
||||
def __init__(self, options=None):
|
||||
"""
|
||||
Creates a new CPLEXSolver.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
options: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"mip_display": 5} to increase the log verbosity.
|
||||
"""
|
||||
super().__init__()
|
||||
self._pyomo_solver = pe.SolverFactory('cplex_persistent')
|
||||
self._pyomo_solver.options["randomseed"] = randint(low=0, high=1000).rvs()
|
||||
self._pyomo_solver.options["mip_display"] = 4
|
||||
if options is not None:
|
||||
for (key, value) in options.items():
|
||||
self._pyomo_solver.options[key] = value
|
||||
|
||||
def set_threads(self, threads):
|
||||
self._pyomo_solver.options["threads"] = threads
|
||||
|
||||
def set_time_limit(self, time_limit):
|
||||
self._pyomo_solver.options["timelimit"] = time_limit
|
||||
|
||||
def set_gap_tolerance(self, gap_tolerance):
|
||||
self._pyomo_solver.options["mip_tolerances_mipgap"] = gap_tolerance
|
||||
|
||||
def solve_lp(self, tee=False):
|
||||
import cplex
|
||||
lp = self._pyomo_solver._solver_model
|
||||
var_types = lp.variables.get_types()
|
||||
n_vars = len(var_types)
|
||||
lp.set_problem_type(cplex.Cplex.problem_type.LP)
|
||||
results = self._pyomo_solver.solve(tee=tee)
|
||||
lp.variables.set_types(zip(range(n_vars), var_types))
|
||||
return {
|
||||
"Optimal value": results["Problem"][0]["Lower bound"],
|
||||
}
|
||||
|
||||
def _get_warm_start_regexp(self):
|
||||
return "MIP start .* with objective ([0-9.e+-]*)\\."
|
||||
|
||||
def _get_threads_option_name(self):
|
||||
return "threads"
|
||||
|
||||
def _get_time_limit_option_name(self):
|
||||
return "timelimit"
|
||||
|
||||
def _get_gap_tolerance_option_name(self):
|
||||
return "mip_gap_tolerances_mipgap"
|
||||
@ -0,0 +1,95 @@
|
||||
# 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
|
||||
import sys
|
||||
from io import StringIO
|
||||
|
||||
import pyomo.environ as pe
|
||||
from miplearn.solvers import RedirectOutput
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from scipy.stats import randint
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class GurobiSolver(InternalSolver):
|
||||
def __init__(self,
|
||||
use_lazy_callbacks=False,
|
||||
options=None):
|
||||
"""
|
||||
Creates a new GurobiSolver.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
use_lazy_callbacks: bool
|
||||
If true, lazy constraints will be enforced via lazy callbacks.
|
||||
Otherwise, they will be enforced via a simple solve-check loop.
|
||||
options: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.
|
||||
"""
|
||||
super().__init__()
|
||||
self._use_lazy_callbacks = use_lazy_callbacks
|
||||
self._pyomo_solver = pe.SolverFactory('gurobi_persistent')
|
||||
self._pyomo_solver.options["Seed"] = randint(low=0, high=1000).rvs()
|
||||
if options is not None:
|
||||
for (key, value) in options.items():
|
||||
self._pyomo_solver.options[key] = value
|
||||
|
||||
def solve(self, tee=False):
|
||||
if self._use_lazy_callbacks:
|
||||
return self._solve_with_callbacks(tee)
|
||||
else:
|
||||
return super().solve(tee)
|
||||
|
||||
def _solve_with_callbacks(self, tee):
|
||||
from gurobipy import GRB
|
||||
|
||||
def cb(cb_model, cb_opt, cb_where):
|
||||
if cb_where == GRB.Callback.MIPSOL:
|
||||
cb_opt.cbGetSolution(self._all_vars)
|
||||
logger.debug("Finding violated constraints...")
|
||||
violations = self.instance.find_violations(cb_model)
|
||||
self.instance.found_violations += violations
|
||||
logger.debug(" %d violations found" % len(violations))
|
||||
for v in violations:
|
||||
cut = self.instance.build_lazy_constraint(cb_model, v)
|
||||
cb_opt.cbLazy(cut)
|
||||
|
||||
if hasattr(self.instance, "find_violations"):
|
||||
self._pyomo_solver.options["LazyConstraints"] = 1
|
||||
self._pyomo_solver.set_callback(cb)
|
||||
self.instance.found_violations = []
|
||||
print(self._is_warm_start_available)
|
||||
streams = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
with RedirectOutput(streams):
|
||||
results = self._pyomo_solver.solve(tee=True,
|
||||
warmstart=self._is_warm_start_available)
|
||||
self._pyomo_solver.set_callback(None)
|
||||
node_count = int(self._pyomo_solver._solver_model.getAttr("NodeCount"))
|
||||
log = streams[0].getvalue()
|
||||
return {
|
||||
"Lower bound": results["Problem"][0]["Lower bound"],
|
||||
"Upper bound": results["Problem"][0]["Upper bound"],
|
||||
"Wallclock time": results["Solver"][0]["Wallclock time"],
|
||||
"Nodes": max(1, node_count),
|
||||
"Sense": self._obj_sense,
|
||||
"Log": log,
|
||||
"Warm start value": self.extract_warm_start_value(log),
|
||||
}
|
||||
|
||||
def _get_warm_start_regexp(self):
|
||||
return "MIP start with objective ([0-9.e+-]*)"
|
||||
|
||||
def _get_threads_option_name(self):
|
||||
return "Threads"
|
||||
|
||||
def _get_time_limit_option_name(self):
|
||||
return "TimeLimit"
|
||||
|
||||
def _get_gap_tolerance_option_name(self):
|
||||
return "MIPGap"
|
||||
@ -0,0 +1,277 @@
|
||||
# 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
|
||||
import re
|
||||
import sys
|
||||
from abc import ABC, abstractmethod
|
||||
from io import StringIO
|
||||
|
||||
import pyomo
|
||||
import pyomo.environ as pe
|
||||
from pyomo.core import Var
|
||||
|
||||
from . import RedirectOutput
|
||||
from ..instance import Instance
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class InternalSolver(ABC):
|
||||
"""
|
||||
The MIP solver used internaly by LearningSolver.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
instance: miplearn.Instance
|
||||
The MIPLearn instance currently loaded to the solver
|
||||
model: pyomo.core.ConcreteModel
|
||||
The Pyomo model currently loaded on the solver
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.instance = None
|
||||
self.model = None
|
||||
self._all_vars = None
|
||||
self._bin_vars = None
|
||||
self._is_warm_start_available = False
|
||||
self._pyomo_solver = None
|
||||
self._obj_sense = None
|
||||
self._varname_to_var = {}
|
||||
|
||||
def solve_lp(self, tee=False):
|
||||
"""
|
||||
Solves the LP relaxation of the currently loaded instance.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
tee: bool
|
||||
If true, prints the solver log to the screen.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary of solver statistics containing the following keys:
|
||||
"Optimal value".
|
||||
"""
|
||||
for var in self._bin_vars:
|
||||
lb, ub = var.bounds
|
||||
var.setlb(lb)
|
||||
var.setub(ub)
|
||||
var.domain = pyomo.core.base.set_types.Reals
|
||||
self._pyomo_solver.update_var(var)
|
||||
results = self._pyomo_solver.solve(tee=tee)
|
||||
for var in self._bin_vars:
|
||||
var.domain = pyomo.core.base.set_types.Binary
|
||||
self._pyomo_solver.update_var(var)
|
||||
return {
|
||||
"Optimal value": results["Problem"][0]["Lower bound"],
|
||||
}
|
||||
|
||||
def get_solution(self):
|
||||
"""
|
||||
Returns current solution found by the solver.
|
||||
|
||||
If called after `solve`, returns the best primal solution found during
|
||||
the search. If called after `solve_lp`, returns the optimal solution
|
||||
to the LP relaxation.
|
||||
|
||||
The solution is a dictionary `sol`, where the optimal value of `var[idx]`
|
||||
is given by `sol[var][idx]`.
|
||||
"""
|
||||
solution = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
solution[str(var)] = {}
|
||||
for index in var:
|
||||
solution[str(var)][index] = var[index].value
|
||||
return solution
|
||||
|
||||
def set_warm_start(self, solution):
|
||||
"""
|
||||
Sets the warm start to be used by the solver.
|
||||
|
||||
The solution should be a dictionary following the same format as the
|
||||
one produced by `get_solution`. Only one warm start is currently
|
||||
supported. Calling this function when a warm start already exists will
|
||||
remove the previous warm start.
|
||||
"""
|
||||
self.clear_warm_start()
|
||||
count_total, count_fixed = 0, 0
|
||||
for var_name in solution:
|
||||
var = self._varname_to_var[var_name]
|
||||
for index in solution[var_name]:
|
||||
count_total += 1
|
||||
var[index].value = solution[var_name][index]
|
||||
if solution[var_name][index] is not None:
|
||||
count_fixed += 1
|
||||
if count_fixed > 0:
|
||||
self._is_warm_start_available = True
|
||||
logger.info("Setting start values for %d variables (out of %d)" %
|
||||
(count_fixed, count_total))
|
||||
|
||||
def clear_warm_start(self):
|
||||
"""
|
||||
Removes any existing warm start from the solver.
|
||||
"""
|
||||
for var in self._all_vars:
|
||||
if not var.fixed:
|
||||
var.value = None
|
||||
self._is_warm_start_available = False
|
||||
|
||||
def set_instance(self, instance, model=None):
|
||||
"""
|
||||
Loads the given instance into the solver.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
instance: miplearn.Instance
|
||||
The instance to be loaded.
|
||||
model: pyomo.core.ConcreteModel
|
||||
The corresponding Pyomo model. If not provided, it will be
|
||||
generated by calling `instance.to_model()`.
|
||||
"""
|
||||
if model is None:
|
||||
model = instance.to_model()
|
||||
assert isinstance(instance, Instance)
|
||||
assert isinstance(model, pe.ConcreteModel)
|
||||
self.instance = instance
|
||||
self.model = model
|
||||
self._pyomo_solver.set_instance(model)
|
||||
|
||||
# Update objective sense
|
||||
self._obj_sense = "max"
|
||||
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
|
||||
self._obj_sense = "min"
|
||||
|
||||
# Update variables
|
||||
self._all_vars = []
|
||||
self._bin_vars = []
|
||||
self._varname_to_var = {}
|
||||
for var in model.component_objects(Var):
|
||||
self._varname_to_var[var.name] = var
|
||||
for idx in var:
|
||||
self._all_vars += [var[idx]]
|
||||
if var[idx].domain == pyomo.core.base.set_types.Binary:
|
||||
self._bin_vars += [var[idx]]
|
||||
|
||||
def fix(self, solution):
|
||||
"""
|
||||
Fixes the values of a subset of decision variables.
|
||||
|
||||
The values should be provided in the dictionary format generated by
|
||||
`get_solution`. Missing values in the solution indicate variables
|
||||
that should be left free.
|
||||
"""
|
||||
count_total, count_fixed = 0, 0
|
||||
for varname in solution:
|
||||
for index in solution[varname]:
|
||||
var = self._varname_to_var[varname]
|
||||
count_total += 1
|
||||
if solution[varname][index] is None:
|
||||
continue
|
||||
count_fixed += 1
|
||||
var[index].fix(solution[varname][index])
|
||||
self._pyomo_solver.update_var(var[index])
|
||||
logger.info("Fixing values for %d variables (out of %d)" %
|
||||
(count_fixed, count_total))
|
||||
|
||||
def add_constraint(self, constraint):
|
||||
"""
|
||||
Adds a single constraint to the model.
|
||||
"""
|
||||
self._pyomo_solver.add_constraint(constraint)
|
||||
|
||||
def solve(self, tee=False):
|
||||
"""
|
||||
Solves the currently loaded instance.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
tee: bool
|
||||
If true, prints the solver log to the screen.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary of solver statistics containing the following keys:
|
||||
"Lower bound", "Upper bound", "Wallclock time", "Nodes", "Sense",
|
||||
"Log" and "Warm start value".
|
||||
"""
|
||||
total_wallclock_time = 0
|
||||
streams = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
self.instance.found_violations = []
|
||||
while True:
|
||||
logger.debug("Solving MIP...")
|
||||
with RedirectOutput(streams):
|
||||
results = self._pyomo_solver.solve(tee=True,
|
||||
warmstart=self._is_warm_start_available)
|
||||
total_wallclock_time += results["Solver"][0]["Wallclock time"]
|
||||
if not hasattr(self.instance, "find_violations"):
|
||||
break
|
||||
logger.debug("Finding violated constraints...")
|
||||
violations = self.instance.find_violations(self.model)
|
||||
if len(violations) == 0:
|
||||
break
|
||||
self.instance.found_violations += violations
|
||||
logger.debug(" %d violations found" % len(violations))
|
||||
for v in violations:
|
||||
cut = self.instance.build_lazy_constraint(self.model, v)
|
||||
self.add_constraint(cut)
|
||||
|
||||
log = streams[0].getvalue()
|
||||
return {
|
||||
"Lower bound": results["Problem"][0]["Lower bound"],
|
||||
"Upper bound": results["Problem"][0]["Upper bound"],
|
||||
"Wallclock time": total_wallclock_time,
|
||||
"Nodes": 1,
|
||||
"Sense": self._obj_sense,
|
||||
"Log": log,
|
||||
"Warm start value": self.extract_warm_start_value(log),
|
||||
}
|
||||
|
||||
def extract_warm_start_value(self, log):
|
||||
"""
|
||||
Extracts and returns the objective value of the user-provided MIP start
|
||||
from the provided solver log. If more than one value is found, returns
|
||||
the last one. If no value is present in the logs, returns None.
|
||||
"""
|
||||
ws_value = None
|
||||
for line in log.splitlines():
|
||||
matches = re.findall(self._get_warm_start_regexp(), line)
|
||||
if len(matches) == 0:
|
||||
continue
|
||||
ws_value = float(matches[0])
|
||||
return ws_value
|
||||
|
||||
def set_threads(self, threads):
|
||||
key = self._get_threads_option_name()
|
||||
self._pyomo_solver.options[key] = threads
|
||||
|
||||
def set_time_limit(self, time_limit):
|
||||
key = self._get_time_limit_option_name()
|
||||
self._pyomo_solver.options[key] = time_limit
|
||||
|
||||
def set_gap_tolerance(self, gap_tolerance):
|
||||
key = self._get_gap_tolerance_option_name()
|
||||
self._pyomo_solver.options[key] = gap_tolerance
|
||||
|
||||
@abstractmethod
|
||||
def _get_warm_start_regexp(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def _get_threads_option_name(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def _get_time_limit_option_name(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def _get_gap_tolerance_option_name(self):
|
||||
pass
|
||||
|
||||
|
||||
@ -0,0 +1,169 @@
|
||||
# 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__
|
||||
@ -0,0 +1,4 @@
|
||||
# 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.
|
||||
|
||||
Loading…
Reference in new issue