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389 lines
13 KiB
389 lines
13 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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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 random import randint
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from typing import List, Any, Dict, Union, Tuple, Optional
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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 GurobiSolver(InternalSolver):
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def __init__(
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self,
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params=None,
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lazy_cb_frequency=1,
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):
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"""
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An InternalSolver backed by Gurobi's Python API (without Pyomo).
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Parameters
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----------
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params
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Parameters to pass to Gurobi. For example, params={"MIPGap": 1e-3}
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sets the gap tolerance to 1e-3.
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lazy_cb_frequency
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If 1, calls lazy constraint callbacks whenever an integer solution
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is found. If 2, calls it also at every node, after solving the
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LP relaxation of that node.
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"""
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if params is None:
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params = {}
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params["InfUnbdInfo"] = True
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import gurobipy
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self.gp = gurobipy
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self.GRB = gurobipy.GRB
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self.instance = None
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self.model = None
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self.params = params
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self._all_vars: Dict = {}
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self._bin_vars = None
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self.cb_where = None
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assert lazy_cb_frequency in [1, 2]
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if lazy_cb_frequency == 1:
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self.lazy_cb_where = [self.GRB.Callback.MIPSOL]
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else:
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self.lazy_cb_where = [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]
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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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self._raise_if_callback()
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if model is None:
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model = instance.to_model()
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assert isinstance(model, self.gp.Model)
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self.instance = instance
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self.model = model
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self.model.update()
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self._update_vars()
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def _raise_if_callback(self) -> None:
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if self.cb_where is not None:
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raise Exception("method cannot be called from a callback")
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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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idx: Union[Tuple, List[int], int]
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for var in self.model.getVars():
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m = re.search(r"([^[]*)\[(.*)]", var.varName)
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if m is None:
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name = var.varName
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idx = [0]
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else:
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name = m.group(1)
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idx = tuple(
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int(k) if k.isdecimal() else k for k in m.group(2).split(",")
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)
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if len(idx) == 1:
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idx = idx[0]
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if name not in self._all_vars:
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self._all_vars[name] = {}
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self._all_vars[name][idx] = var
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if var.vtype != "C":
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if name not in self._bin_vars:
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self._bin_vars[name] = {}
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self._bin_vars[name][idx] = var
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def _apply_params(self, streams: List[Any]) -> None:
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with RedirectOutput(streams):
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for (name, value) in self.params.items():
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self.model.setParam(name, value)
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if "seed" not in [k.lower() for k in self.params.keys()]:
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self.model.setParam("Seed", randint(0, 1_000_000))
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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._raise_if_callback()
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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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self._apply_params(streams)
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for (varname, vardict) in self._bin_vars.items():
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for (idx, var) in vardict.items():
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var.vtype = self.GRB.CONTINUOUS
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var.lb = 0.0
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var.ub = 1.0
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with RedirectOutput(streams):
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self.model.optimize()
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for (varname, vardict) in self._bin_vars.items():
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for (idx, var) in vardict.items():
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var.vtype = self.GRB.BINARY
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log = streams[0].getvalue()
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opt_value = None
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if not self.is_infeasible():
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opt_value = self.model.objVal
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return {
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"Optimal value": opt_value,
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"Log": log,
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}
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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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self._raise_if_callback()
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def cb_wrapper(cb_model, cb_where):
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try:
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self.cb_where = cb_where
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if cb_where in self.lazy_cb_where:
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lazy_cb(self, self.model)
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except:
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logger.exception("callback error")
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finally:
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self.cb_where = None
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if lazy_cb:
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self.params["LazyConstraints"] = 1
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total_wallclock_time = 0
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total_nodes = 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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self._apply_params(streams)
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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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with RedirectOutput(streams):
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if lazy_cb is None:
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self.model.optimize()
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else:
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self.model.optimize(cb_wrapper)
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total_wallclock_time += self.model.runtime
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total_nodes += int(self.model.nodeCount)
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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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ub, lb = None, None
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sense = "min" if self.model.modelSense == 1 else "max"
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if self.model.solCount > 0:
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if self.model.modelSense == 1:
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lb = self.model.objBound
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ub = self.model.objVal
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else:
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lb = self.model.objVal
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ub = self.model.objBound
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ws_value = self._extract_warm_start_value(log)
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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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"Nodes": total_nodes,
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"Sense": sense,
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"Log": log,
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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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self._raise_if_callback()
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if self.model.solCount == 0:
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return None
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solution: Dict = {}
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for (varname, vardict) in self._all_vars.items():
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solution[varname] = {}
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for (idx, var) in vardict.items():
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solution[varname][idx] = var.x
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return solution
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def set_warm_start(self, solution: Dict) -> None:
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self._raise_if_callback()
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self._clear_warm_start()
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count_fixed, count_total = 0, 0
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for (varname, vardict) in solution.items():
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for (idx, value) in vardict.items():
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count_total += 1
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if value is not None:
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count_fixed += 1
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self._all_vars[varname][idx].start = value
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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 get_sense(self):
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if self.model.modelSense == 1:
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return "min"
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else:
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return "max"
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def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
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var = self._all_vars[var_name][index]
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return self._get_value(var)
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def is_infeasible(self) -> bool:
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return self.model.status in [self.GRB.INFEASIBLE, self.GRB.INF_OR_UNBD]
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def get_dual(self, cid):
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c = self.model.getConstrByName(cid)
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if self.is_infeasible():
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return c.farkasDual
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else:
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return c.pi
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def _get_value(self, var: Any) -> Optional[float]:
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if self.cb_where == self.GRB.Callback.MIPSOL:
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return self.model.cbGetSolution(var)
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elif self.cb_where == self.GRB.Callback.MIPNODE:
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return self.model.cbGetNodeRel(var)
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elif self.cb_where is None:
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if self.is_infeasible():
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return None
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else:
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return var.x
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else:
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raise Exception(
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"get_value cannot be called from cb_where=%s" % self.cb_where
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)
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def get_empty_solution(self) -> Dict:
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self._raise_if_callback()
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solution: Dict = {}
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for (varname, vardict) in self._all_vars.items():
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solution[varname] = {}
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for (idx, var) in vardict.items():
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solution[varname][idx] = None
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return solution
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def add_constraint(self, constraint, name=""):
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if type(constraint) is tuple:
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lhs, sense, rhs, name = constraint
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if self.cb_where in [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]:
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self.model.cbLazy(lhs, sense, rhs)
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else:
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self.model.addConstr(lhs, sense, rhs, name)
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else:
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if self.cb_where in [self.GRB.Callback.MIPSOL, self.GRB.Callback.MIPNODE]:
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self.model.cbLazy(constraint)
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else:
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self.model.addConstr(constraint, name=name)
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def _clear_warm_start(self) -> None:
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for (varname, vardict) in self._all_vars.items():
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for (idx, var) in vardict.items():
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var.start = self.GRB.UNDEFINED
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def fix(self, solution):
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self._raise_if_callback()
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for (varname, vardict) in solution.items():
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for (idx, value) in vardict.items():
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if value is None:
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continue
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var = self._all_vars[varname][idx]
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var.vtype = self.GRB.CONTINUOUS
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var.lb = value
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var.ub = value
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def get_constraint_ids(self):
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self._raise_if_callback()
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self.model.update()
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return [c.ConstrName for c in self.model.getConstrs()]
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def extract_constraint(self, cid):
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self._raise_if_callback()
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constr = self.model.getConstrByName(cid)
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cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
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self.model.remove(constr)
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return cobj
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def is_constraint_satisfied(self, cobj, tol=1e-5):
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lhs, sense, rhs, name = cobj
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if self.cb_where is not None:
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lhs_value = lhs.getConstant()
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for i in range(lhs.size()):
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var = lhs.getVar(i)
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coeff = lhs.getCoeff(i)
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lhs_value += self._get_value(var) * coeff
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else:
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lhs_value = lhs.getValue()
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if sense == "<":
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return lhs_value <= rhs + tol
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elif sense == ">":
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return lhs_value >= rhs - tol
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elif sense == "=":
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return abs(rhs - lhs_value) < abs(tol)
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else:
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raise Exception("Unknown sense: %s" % sense)
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def get_inequality_slacks(self) -> Dict[str, float]:
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ineqs = [c for c in self.model.getConstrs() if c.sense != "="]
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return {c.ConstrName: c.Slack for c in ineqs}
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def set_constraint_sense(self, cid, sense):
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c = self.model.getConstrByName(cid)
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c.Sense = sense
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def get_constraint_sense(self, cid):
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c = self.model.getConstrByName(cid)
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return c.Sense
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def set_constraint_rhs(self, cid, rhs):
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c = self.model.getConstrByName(cid)
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c.RHS = rhs
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def relax(self) -> None:
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self.model = self.model.relax()
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self._update_vars()
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def _extract_warm_start_value(self, log: str) -> Optional[float]:
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ws = self.__extract(log, "MIP start with objective ([0-9.e+-]*)")
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if ws is None:
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return None
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return float(ws)
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@staticmethod
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def __extract(
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log: str,
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regexp: str,
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default: Optional[str] = None,
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) -> Optional[str]:
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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 __getstate__(self):
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return {
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"params": self.params,
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"lazy_cb_where": self.lazy_cb_where,
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}
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def __setstate__(self, state):
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from gurobipy import GRB
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self.params = state["params"]
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self.lazy_cb_where = state["lazy_cb_where"]
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self.GRB = GRB
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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.cb_where = None
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