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318 lines
10 KiB
318 lines
10 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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import logging
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import re
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import sys
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from io import StringIO
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from typing import Any, List, Dict, Optional
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import pyomo
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from pyomo import environ as pe
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from pyomo.core import Var, Constraint
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from pyomo.opt import TerminationCondition
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from miplearn.instance import Instance
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from miplearn.solvers import RedirectOutput
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from miplearn.solvers.internal import (
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InternalSolver,
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LPSolveStats,
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IterationCallback,
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LazyCallback,
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MIPSolveStats,
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)
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from miplearn.types import VarIndex
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logger = logging.getLogger(__name__)
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class BasePyomoSolver(InternalSolver):
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"""
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Base class for all Pyomo solvers.
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"""
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def __init__(
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self,
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solver_factory,
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params,
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):
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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 = solver_factory
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self._obj_sense = None
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self._varname_to_var = {}
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self._cname_to_constr = {}
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self._termination_condition = None
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for (key, value) in params.items():
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self._pyomo_solver.options[key] = value
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def solve_lp(
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self,
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tee: bool = False,
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) -> LPSolveStats:
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self.relax()
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streams: List[Any] = [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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self._restore_integrality()
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opt_value = None
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if not self.is_infeasible():
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opt_value = results["Problem"][0]["Lower bound"]
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return {
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"Optimal value": opt_value,
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"Log": streams[0].getvalue(),
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}
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def _restore_integrality(self) -> None:
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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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def solve(
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self,
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tee: bool = False,
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iteration_cb: IterationCallback = None,
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lazy_cb: LazyCallback = None,
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) -> MIPSolveStats:
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if lazy_cb is not None:
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raise Exception("lazy callback not supported")
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total_wallclock_time = 0
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streams: List[Any] = [StringIO()]
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if tee:
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streams += [sys.stdout]
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if iteration_cb is None:
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iteration_cb = lambda: False
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self.instance.found_violated_lazy_constraints = []
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self.instance.found_violated_user_cuts = []
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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(
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tee=True,
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warmstart=self._is_warm_start_available,
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)
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total_wallclock_time += results["Solver"][0]["Wallclock time"]
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should_repeat = iteration_cb()
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if not should_repeat:
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break
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log = streams[0].getvalue()
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node_count = self._extract_node_count(log)
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ws_value = self._extract_warm_start_value(log)
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self._termination_condition = results["Solver"][0]["Termination condition"]
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lb, ub = None, None
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if not self.is_infeasible():
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lb = results["Problem"][0]["Lower bound"]
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ub = results["Problem"][0]["Upper bound"]
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stats: MIPSolveStats = {
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"Lower bound": lb,
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"Upper bound": ub,
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"Wallclock time": total_wallclock_time,
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"Sense": self._obj_sense,
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"Log": log,
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"Nodes": node_count,
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"Warm start value": ws_value,
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"LP value": None,
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}
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return stats
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def get_solution(self) -> Optional[Dict]:
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if self.is_infeasible():
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return None
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solution: Dict = {}
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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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if var[index].fixed:
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continue
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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: Dict) -> None:
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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(
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"Setting start values for %d variables (out of %d)"
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% (count_fixed, count_total)
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)
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def set_instance(
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self,
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instance: Instance,
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model: Any = None,
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) -> None:
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if model is None:
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model = instance.to_model()
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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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self._update_obj()
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self._update_vars()
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self._update_constrs()
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def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
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if self.is_infeasible():
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return None
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else:
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var = self._varname_to_var[var_name]
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return var[index].value
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def get_empty_solution(self) -> Dict:
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solution: Dict = {}
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for var in self.model.component_objects(Var):
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svar = str(var)
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solution[svar] = {}
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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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solution[svar][index] = None
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return solution
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def _clear_warm_start(self) -> None:
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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 _update_obj(self) -> None:
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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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def _update_vars(self) -> None:
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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 self.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 _update_constrs(self) -> None:
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self._cname_to_constr = {}
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for constr in self.model.component_objects(Constraint):
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self._cname_to_constr[constr.name] = constr
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def fix(self, solution):
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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(
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"Fixing values for %d variables (out of %d)"
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% (
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count_fixed,
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count_total,
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)
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)
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def add_constraint(self, constraint):
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self._pyomo_solver.add_constraint(constraint)
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self._update_constrs()
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@staticmethod
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def __extract(
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log: str,
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regexp: Optional[str],
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default: Optional[str] = None,
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) -> Optional[str]:
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if regexp is None:
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return default
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value = default
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for line in log.splitlines():
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matches = re.findall(regexp, line)
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if len(matches) == 0:
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continue
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value = matches[0]
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return value
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def _extract_warm_start_value(self, log: str) -> Optional[float]:
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value = self.__extract(log, self._get_warm_start_regexp())
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if value is None:
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return None
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return float(value)
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def _extract_node_count(self, log: str) -> Optional[int]:
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value = self.__extract(log, self._get_node_count_regexp())
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if value is None:
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return None
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return int(value)
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def get_constraint_ids(self):
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return list(self._cname_to_constr.keys())
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def _get_warm_start_regexp(self) -> Optional[str]:
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return None
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def _get_node_count_regexp(self) -> Optional[str]:
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return None
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def relax(self) -> None:
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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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def get_inequality_slacks(self) -> Dict[str, float]:
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result: Dict[str, float] = {}
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for (cname, cobj) in self._cname_to_constr.items():
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if cobj.equality:
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continue
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result[cname] = cobj.slack()
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return result
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def get_constraint_sense(self, cid: str) -> str:
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cobj = self._cname_to_constr[cid]
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has_ub = cobj.has_ub()
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has_lb = cobj.has_lb()
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assert (not has_lb) or (not has_ub), "range constraints not supported"
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if has_lb:
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return ">"
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elif has_ub:
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return "<"
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else:
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return "="
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def set_constraint_sense(self, cid: str, sense: str) -> None:
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raise Exception("Not implemented")
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def extract_constraint(self, cid: str) -> Constraint:
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raise Exception("Not implemented")
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def is_constraint_satisfied(self, cobj: Constraint) -> bool:
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raise Exception("Not implemented")
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def is_infeasible(self) -> bool:
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return self._termination_condition == TerminationCondition.infeasible
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def get_dual(self, cid):
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raise Exception("Not implemented")
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def get_sense(self) -> str:
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return self._obj_sense
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