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492 lines
16 KiB
492 lines
16 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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 numpy as np
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import pyomo
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from overrides import overrides
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from pyomo import environ as pe
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from pyomo.core import Var
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from pyomo.core.base import _GeneralVarData
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from pyomo.core.base.constraint import ConstraintList
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from pyomo.core.expr.numeric_expr import SumExpression, MonomialTermExpression
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from pyomo.opt import TerminationCondition
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from pyomo.opt.base.solvers import SolverFactory
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from miplearn.instance.base 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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Constraint,
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)
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from miplearn.types import (
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SolverParams,
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UserCutCallback,
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Solution,
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VariableName,
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Category,
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)
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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: SolverFactory,
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params: SolverParams,
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) -> None:
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self.instance: Optional[Instance] = None
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self.model: Optional[pe.ConcreteModel] = None
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self.params = params
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self._all_vars: List[pe.Var] = []
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self._bin_vars: List[pe.Var] = []
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self._is_warm_start_available: bool = False
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self._pyomo_solver: SolverFactory = solver_factory
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self._obj_sense: str = "min"
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self._varname_to_var: Dict[str, pe.Var] = {}
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self._cname_to_constr: Dict[str, pe.Constraint] = {}
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self._termination_condition: str = ""
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for (key, value) in params.items():
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self._pyomo_solver.options[key] = value
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@overrides
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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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"LP value": opt_value,
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"LP 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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@overrides
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def solve(
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self,
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tee: bool = False,
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iteration_cb: Optional[IterationCallback] = None,
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lazy_cb: Optional[LazyCallback] = None,
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user_cut_cb: Optional[UserCutCallback] = None,
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) -> MIPSolveStats:
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assert lazy_cb is None, "callbacks are not currently supported"
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assert user_cut_cb is None, "callbacks are not currently 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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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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"MIP log": log,
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"Nodes": node_count,
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"Warm start value": ws_value,
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}
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return stats
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@overrides
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def get_solution(self) -> Optional[Solution]:
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assert self.model is not None
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if self.is_infeasible():
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return None
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solution: Solution = {}
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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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solution[f"{var}[{index}]"] = var[index].value
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return solution
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@overrides
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def get_variable_names(self) -> List[VariableName]:
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assert self.model is not None
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variables: List[VariableName] = []
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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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variables += [f"{var}[{index}]"]
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return variables
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@overrides
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def set_warm_start(self, solution: Solution) -> None:
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self._clear_warm_start()
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count_fixed = 0
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for (var_name, value) in solution.items():
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if value is None:
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continue
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var = self._varname_to_var[var_name]
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var.value = solution[var_name]
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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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@overrides
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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.model.extra_constraints = ConstraintList()
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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 _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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assert self.model is not 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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for idx in var:
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self._varname_to_var[f"{var.name}[{idx}]"] = var[idx]
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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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assert self.model is not None
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self._cname_to_constr.clear()
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for constr in self.model.component_objects(pyomo.core.Constraint):
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if isinstance(constr, pe.ConstraintList):
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for idx in constr:
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self._cname_to_constr[f"{constr.name}[{idx}]"] = constr[idx]
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else:
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self._cname_to_constr[constr.name] = constr
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@overrides
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def fix(self, solution: Solution) -> None:
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for (varname, value) in solution.items():
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if value is None:
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continue
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var = self._varname_to_var[varname]
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var.fix(value)
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self._pyomo_solver.update_var(var)
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@overrides
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def add_constraint(
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self,
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constr: Any,
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name: str,
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) -> None:
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assert self.model is not None
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if isinstance(constr, Constraint):
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lhs = 0.0
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for (varname, coeff) in constr.lhs.items():
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var = self._varname_to_var[varname]
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lhs += var * coeff
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if constr.sense == "=":
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expr = lhs == constr.rhs
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elif constr.sense == "<":
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expr = lhs <= constr.rhs
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else:
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expr = lhs >= constr.rhs
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cl = pe.Constraint(expr=expr, name=name)
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self.model.add_component(name, cl)
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self._pyomo_solver.add_constraint(cl)
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self._cname_to_constr[name] = cl
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else:
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self._pyomo_solver.add_constraint(constr)
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@overrides
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def remove_constraint(self, name: str) -> None:
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assert self.model is not None
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constr = self._cname_to_constr[name]
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del self._cname_to_constr[name]
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self.model.del_component(constr)
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self._pyomo_solver.remove_constraint(constr)
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@overrides
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def is_constraint_satisfied(self, constr: Constraint, tol: float = 1e-6) -> bool:
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lhs = 0.0
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for (varname, coeff) in constr.lhs.items():
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var = self._varname_to_var[varname]
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lhs += var.value * coeff
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if constr.sense == "<":
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return lhs <= constr.rhs + tol
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elif constr.sense == ">":
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return lhs >= constr.rhs - tol
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else:
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return abs(constr.rhs - lhs) < abs(tol)
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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_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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@overrides
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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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@overrides
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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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@overrides
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def is_infeasible(self) -> bool:
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return self._termination_condition == TerminationCondition.infeasible
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@overrides
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def get_dual(self, cid: str) -> float:
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raise NotImplementedError()
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@overrides
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def get_sense(self) -> str:
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return self._obj_sense
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@overrides
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def build_test_instance_infeasible(self) -> Instance:
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return PyomoTestInstanceInfeasible()
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@overrides
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def build_test_instance_redundancy(self) -> Instance:
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return PyomoTestInstanceRedundancy()
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@overrides
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def build_test_instance_knapsack(self) -> Instance:
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return PyomoTestInstanceKnapsack(
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weights=[23.0, 26.0, 20.0, 18.0],
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prices=[505.0, 352.0, 458.0, 220.0],
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capacity=67.0,
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)
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@overrides
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def get_constraints(self) -> Dict[str, Constraint]:
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assert self.model is not None
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constraints = {}
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for constr in self.model.component_objects(pyomo.core.Constraint):
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if isinstance(constr, pe.ConstraintList):
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for idx in constr:
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name = f"{constr.name}[{idx}]"
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assert name not in constraints
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constraints[name] = self._parse_pyomo_constraint(constr[idx])
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else:
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name = constr.name
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assert name not in constraints
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constraints[name] = self._parse_pyomo_constraint(constr)
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return constraints
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@staticmethod
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def _parse_pyomo_constraint(c: pyomo.core.Constraint) -> Constraint:
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# Extract RHS and sense
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has_ub = c.has_ub()
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has_lb = c.has_lb()
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assert (
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(not has_lb) or (not has_ub) or c.upper() == c.lower()
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), "range constraints not supported"
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if has_lb:
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sense = ">"
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rhs = c.lower()
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elif has_ub:
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sense = "<"
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rhs = c.upper()
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else:
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sense = "="
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rhs = c.upper()
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# Extract LHS
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lhs = {}
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if isinstance(c.body, SumExpression):
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for term in c.body._args_:
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if isinstance(term, MonomialTermExpression):
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lhs[term._args_[1].name] = term._args_[0]
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elif isinstance(term, _GeneralVarData):
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lhs[term.name] = 1.0
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else:
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raise Exception(f"Unknown term type: {term.__class__.__name__}")
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elif isinstance(c.body, _GeneralVarData):
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lhs[c.body.name] = 1.0
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else:
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raise Exception(f"Unknown expression type: {c.body.__class__.__name__}")
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# Build constraint
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return Constraint(
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lhs=lhs,
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rhs=rhs,
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sense=sense,
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)
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@overrides
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def are_callbacks_supported(self) -> bool:
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return False
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class PyomoTestInstanceInfeasible(Instance):
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@overrides
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def to_model(self) -> pe.ConcreteModel:
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model = pe.ConcreteModel()
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model.x = pe.Var([0], domain=pe.Binary)
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model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
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model.eq = pe.Constraint(expr=model.x[0] >= 2)
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return model
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class PyomoTestInstanceRedundancy(Instance):
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@overrides
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def to_model(self) -> pe.ConcreteModel:
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model = pe.ConcreteModel()
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model.x = pe.Var([0, 1], domain=pe.Binary)
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model.OBJ = pe.Objective(expr=model.x[0] + model.x[1], sense=pe.maximize)
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model.eq1 = pe.Constraint(expr=model.x[0] + model.x[1] <= 1)
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model.eq2 = pe.Constraint(expr=model.x[0] + model.x[1] <= 2)
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return model
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class PyomoTestInstanceKnapsack(Instance):
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"""
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Simpler (one-dimensional) Knapsack Problem, used for testing.
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"""
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def __init__(
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self,
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weights: List[float],
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prices: List[float],
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capacity: float,
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) -> None:
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super().__init__()
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self.weights = weights
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self.prices = prices
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self.capacity = capacity
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self.varname_to_item: Dict[VariableName, int] = {
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f"x[{i}]": i for i in range(len(self.weights))
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}
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@overrides
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def to_model(self) -> pe.ConcreteModel:
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model = pe.ConcreteModel()
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items = range(len(self.weights))
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model.x = pe.Var(items, domain=pe.Binary)
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model.OBJ = pe.Objective(
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expr=sum(model.x[v] * self.prices[v] for v in items),
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sense=pe.maximize,
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)
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model.eq_capacity = pe.Constraint(
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expr=sum(model.x[v] * self.weights[v] for v in items) <= self.capacity
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)
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return model
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@overrides
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def get_instance_features(self) -> List[float]:
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return [
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self.capacity,
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np.average(self.weights),
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]
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@overrides
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def get_variable_features(self, var_name: VariableName) -> List[Category]:
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item = self.varname_to_item[var_name]
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return [
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self.weights[item],
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self.prices[item],
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]
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