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644 lines
21 KiB
644 lines
21 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, Tuple
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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, Suffix, Objective
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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, _none_if_empty
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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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Variables,
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Constraints,
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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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self._has_lp_solution = False
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self._has_mip_solution = False
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self._obj: Dict[str, float] = {}
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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 add_constraint(
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self,
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constr: Any,
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) -> None:
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assert self.model is not None
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self._pyomo_solver.add_constraint(constr)
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self._termination_condition = ""
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self._has_lp_solution = False
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self._has_mip_solution = False
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@overrides
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def add_constraints(self, cf: Constraints) -> None:
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assert cf.names is not None
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assert cf.senses is not None
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assert cf.lhs is not None
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assert cf.rhs is not None
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assert self.model is not None
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for (i, name) in enumerate(cf.names):
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lhs = 0.0
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for (varname, coeff) in cf.lhs[i]:
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var = self._varname_to_var[varname]
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lhs += var * coeff
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if cf.senses[i] == "=":
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expr = lhs == cf.rhs[i]
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elif cf.senses[i] == "<":
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expr = lhs <= cf.rhs[i]
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else:
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expr = lhs >= cf.rhs[i]
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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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self._termination_condition = ""
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self._has_lp_solution = False
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self._has_mip_solution = False
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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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@overrides
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def are_constraints_satisfied(
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self,
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cf: Constraints,
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tol: float = 1e-5,
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) -> List[bool]:
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assert cf.names is not None
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assert cf.lhs is not None
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assert cf.rhs is not None
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assert cf.senses is not None
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result = []
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for (i, name) in enumerate(cf.names):
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lhs = 0.0
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for (varname, coeff) in cf.lhs[i]:
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var = self._varname_to_var[varname]
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lhs += var.value * coeff
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if cf.senses[i] == "<":
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result.append(lhs <= cf.rhs[i] + tol)
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elif cf.senses[i] == ">":
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result.append(lhs >= cf.rhs[i] - tol)
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else:
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result.append(abs(cf.rhs[i] - lhs) < tol)
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return result
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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_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 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 get_constraints(
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self,
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with_static: bool = True,
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with_sa: bool = True,
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with_lhs: bool = True,
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) -> Constraints:
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model = self.model
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assert model is not None
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names: List[str] = []
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rhs: List[float] = []
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lhs: List[List[Tuple[str, float]]] = []
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senses: List[str] = []
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dual_values: List[float] = []
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slacks: List[float] = []
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def _parse_constraint(c: pe.Constraint) -> None:
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assert model is not None
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if with_static:
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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 not has_ub:
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senses.append(">")
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rhs.append(float(c.lower()))
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elif not has_lb:
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senses.append("<")
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rhs.append(float(c.upper()))
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else:
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senses.append("=")
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rhs.append(float(c.upper()))
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if with_lhs:
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# Extract LHS
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lhsc = []
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expr = c.body
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if isinstance(expr, SumExpression):
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for term in expr._args_:
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if isinstance(term, MonomialTermExpression):
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lhsc.append(
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(
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term._args_[1].name,
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float(term._args_[0]),
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)
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)
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elif isinstance(term, _GeneralVarData):
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lhsc.append((term.name, 1.0))
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else:
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raise Exception(
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f"Unknown term type: {term.__class__.__name__}"
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)
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elif isinstance(expr, _GeneralVarData):
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lhsc.append((expr.name, 1.0))
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else:
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raise Exception(
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f"Unknown expression type: {expr.__class__.__name__}"
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)
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lhs.append(lhsc)
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# Extract dual values
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if self._has_lp_solution:
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dual_values.append(model.dual[c])
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# Extract slacks
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if self._has_mip_solution or self._has_lp_solution:
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slacks.append(model.slack[c])
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for constr in 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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names.append(f"{constr.name}[{idx}]")
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_parse_constraint(constr[idx])
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else:
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names.append(constr.name)
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_parse_constraint(constr)
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return Constraints(
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names=_none_if_empty(names),
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rhs=_none_if_empty(np.array(rhs, dtype=float)),
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senses=_none_if_empty(senses),
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lhs=_none_if_empty(lhs),
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slacks=_none_if_empty(np.array(slacks, dtype=float)),
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dual_values=_none_if_empty(np.array(dual_values, dtype=float)),
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)
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@overrides
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def get_constraint_attrs(self) -> List[str]:
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return [
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"dual_values",
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"lhs",
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"names",
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"rhs",
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"senses",
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"slacks",
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]
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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_variables(
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self,
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with_static: bool = True,
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with_sa: bool = True,
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) -> Variables:
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assert self.model is not None
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names: List[str] = []
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types: List[str] = []
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upper_bounds: List[float] = []
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lower_bounds: List[float] = []
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obj_coeffs: List[float] = []
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reduced_costs: List[float] = []
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values: List[float] = []
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for (i, var) in enumerate(self.model.component_objects(pyomo.core.Var)):
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for idx in var:
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v = var[idx]
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# Variable name
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if idx is None:
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names.append(str(var))
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else:
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names.append(f"{var}[{idx}]")
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if with_static:
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# Variable type
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if v.domain == pyomo.core.Binary:
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types.append("B")
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elif v.domain in [
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pyomo.core.Reals,
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pyomo.core.NonNegativeReals,
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pyomo.core.NonPositiveReals,
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pyomo.core.NegativeReals,
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pyomo.core.PositiveReals,
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]:
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types.append("C")
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else:
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raise Exception(f"unknown variable domain: {v.domain}")
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# Bounds
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lb, ub = v.bounds
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upper_bounds.append(float(ub))
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lower_bounds.append(float(lb))
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# Objective coefficient
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if v.name in self._obj:
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obj_coeffs.append(self._obj[v.name])
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else:
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obj_coeffs.append(0.0)
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# Reduced costs
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if self._has_lp_solution:
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reduced_costs.append(self.model.rc[v])
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# Values
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if self._has_lp_solution or self._has_mip_solution:
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values.append(v.value)
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return Variables(
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names=_none_if_empty(names),
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types=_none_if_empty(types),
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upper_bounds=_none_if_empty(np.array(upper_bounds, dtype=float)),
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lower_bounds=_none_if_empty(np.array(lower_bounds, dtype=float)),
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obj_coeffs=_none_if_empty(np.array(obj_coeffs, dtype=float)),
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reduced_costs=_none_if_empty(np.array(reduced_costs, dtype=float)),
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values=_none_if_empty(np.array(values, dtype=float)),
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)
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@overrides
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def get_variable_attrs(self) -> List[str]:
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return [
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"names",
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# "basis_status",
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"categories",
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"lower_bounds",
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"obj_coeffs",
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"reduced_costs",
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# "sa_lb_down",
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# "sa_lb_up",
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# "sa_obj_down",
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# "sa_obj_up",
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# "sa_ub_down",
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# "sa_ub_up",
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"types",
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"upper_bounds",
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"user_features",
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"values",
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]
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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 remove_constraints(self, names: List[str]) -> None:
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assert self.model is not None
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for name in names:
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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 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.model.dual = Suffix(direction=Suffix.IMPORT)
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self.model.rc = Suffix(direction=Suffix.IMPORT)
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self.model.slack = Suffix(direction=Suffix.IMPORT)
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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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@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 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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self._has_mip_solution = False
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self._has_lp_solution = False
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if not self.is_infeasible():
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self._has_mip_solution = True
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lb = results["Problem"][0]["Lower bound"]
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ub = results["Problem"][0]["Upper bound"]
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return MIPSolveStats(
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mip_lower_bound=lb,
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mip_upper_bound=ub,
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mip_wallclock_time=total_wallclock_time,
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mip_sense=self._obj_sense,
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mip_log=log,
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mip_nodes=node_count,
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mip_warm_start_value=ws_value,
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)
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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._termination_condition = results["Solver"][0]["Termination condition"]
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self._restore_integrality()
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opt_value = None
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self._has_lp_solution = False
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self._has_mip_solution = False
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if not self.is_infeasible():
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opt_value = results["Problem"][0]["Lower bound"]
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self._has_lp_solution = True
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return LPSolveStats(
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lp_value=opt_value,
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lp_log=streams[0].getvalue(),
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lp_wallclock_time=results["Solver"][0]["Wallclock time"],
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)
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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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@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_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 _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 _get_node_count_regexp(self) -> Optional[str]:
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return None
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def _get_warm_start_regexp(self) -> Optional[str]:
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return None
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def _parse_pyomo_expr(self, expr: Any) -> Dict[str, float]:
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lhs = {}
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if isinstance(expr, SumExpression):
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for term in expr._args_:
|
|
if isinstance(term, MonomialTermExpression):
|
|
lhs[term._args_[1].name] = float(term._args_[0])
|
|
elif isinstance(term, _GeneralVarData):
|
|
lhs[term.name] = 1.0
|
|
else:
|
|
raise Exception(f"Unknown term type: {term.__class__.__name__}")
|
|
elif isinstance(expr, _GeneralVarData):
|
|
lhs[expr.name] = 1.0
|
|
else:
|
|
raise Exception(f"Unknown expression type: {expr.__class__.__name__}")
|
|
return lhs
|
|
|
|
def _relax(self) -> None:
|
|
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)
|
|
|
|
def _restore_integrality(self) -> None:
|
|
for var in self._bin_vars:
|
|
var.domain = pyomo.core.base.set_types.Binary
|
|
self._pyomo_solver.update_var(var)
|
|
|
|
def _update_obj(self) -> None:
|
|
self._obj_sense = "max"
|
|
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
|
|
self._obj_sense = "min"
|
|
|
|
def _update_vars(self) -> None:
|
|
assert self.model is not None
|
|
self._all_vars = []
|
|
self._bin_vars = []
|
|
self._varname_to_var = {}
|
|
for var in self.model.component_objects(Var):
|
|
for idx in var:
|
|
varname = f"{var.name}[{idx}]"
|
|
if idx is None:
|
|
varname = var.name
|
|
self._varname_to_var[varname] = var[idx]
|
|
self._all_vars += [var[idx]]
|
|
if var[idx].domain == pyomo.core.base.set_types.Binary:
|
|
self._bin_vars += [var[idx]]
|
|
for obj in self.model.component_objects(Objective):
|
|
self._obj = self._parse_pyomo_expr(obj.expr)
|
|
break
|
|
|
|
def _update_constrs(self) -> None:
|
|
assert self.model is not None
|
|
self._cname_to_constr.clear()
|
|
for constr in self.model.component_objects(pyomo.core.Constraint):
|
|
if isinstance(constr, pe.ConstraintList):
|
|
for idx in constr:
|
|
self._cname_to_constr[f"{constr.name}[{idx}]"] = constr[idx]
|
|
else:
|
|
self._cname_to_constr[constr.name] = constr
|
|
|
|
|
|
class PyomoTestInstanceInfeasible(Instance):
|
|
@overrides
|
|
def to_model(self) -> pe.ConcreteModel:
|
|
model = pe.ConcreteModel()
|
|
model.x = pe.Var([0], domain=pe.Binary)
|
|
model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
|
|
model.eq = pe.Constraint(expr=model.x[0] >= 2)
|
|
return model
|
|
|
|
|
|
class PyomoTestInstanceKnapsack(Instance):
|
|
"""
|
|
Simpler (one-dimensional) Knapsack Problem, used for testing.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
weights: List[float],
|
|
prices: List[float],
|
|
capacity: float,
|
|
) -> None:
|
|
super().__init__()
|
|
self.weights = weights
|
|
self.prices = prices
|
|
self.capacity = capacity
|
|
|
|
@overrides
|
|
def to_model(self) -> pe.ConcreteModel:
|
|
model = pe.ConcreteModel()
|
|
items = range(len(self.weights))
|
|
model.x = pe.Var(items, domain=pe.Binary)
|
|
model.z = pe.Var(domain=pe.Reals, bounds=(0, self.capacity))
|
|
model.OBJ = pe.Objective(
|
|
expr=sum(model.x[v] * self.prices[v] for v in items),
|
|
sense=pe.maximize,
|
|
)
|
|
model.eq_capacity = pe.Constraint(
|
|
expr=sum(model.x[v] * self.weights[v] for v in items) == model.z
|
|
)
|
|
return model
|
|
|
|
@overrides
|
|
def get_instance_features(self) -> List[float]:
|
|
return [
|
|
self.capacity,
|
|
np.average(self.weights),
|
|
]
|
|
|
|
@overrides
|
|
def get_variable_features(self) -> Dict[str, List[float]]:
|
|
return {
|
|
f"x[{i}]": [
|
|
self.weights[i],
|
|
self.prices[i],
|
|
]
|
|
for i in range(len(self.weights))
|
|
}
|
|
|
|
@overrides
|
|
def get_variable_categories(self) -> Dict[str, str]:
|
|
return {f"x[{i}]": "default" for i in range(len(self.weights))}
|