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314 lines
11 KiB
314 lines
11 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, 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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from typing import Dict, Optional, Callable, Any, List
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import gurobipy as gp
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from gurobipy import GRB, GurobiError
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import numpy as np
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from scipy.sparse import lil_matrix
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from miplearn.h5 import H5File
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from miplearn.solvers.abstract import AbstractModel
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class GurobiModel(AbstractModel):
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_supports_basis_status = True
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_supports_sensitivity_analysis = True
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_supports_node_count = True
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_supports_solution_pool = True
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def __init__(
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self,
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inner: gp.Model,
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lazy_separate: Optional[Callable] = None,
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lazy_enforce: Optional[Callable] = None,
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) -> None:
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self.lazy_separate = lazy_separate
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self.lazy_enforce = lazy_enforce
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self.inner = inner
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self.lazy_constrs_: Optional[List[Any]] = None
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self.where = "default"
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def add_constrs(
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self,
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var_names: np.ndarray,
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constrs_lhs: np.ndarray,
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constrs_sense: np.ndarray,
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constrs_rhs: np.ndarray,
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stats: Optional[Dict] = None,
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) -> None:
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assert len(var_names.shape) == 1
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nvars = len(var_names)
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assert len(constrs_lhs.shape) == 2
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nconstrs = constrs_lhs.shape[0]
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assert constrs_lhs.shape[1] == nvars
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assert constrs_sense.shape == (nconstrs,)
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assert constrs_rhs.shape == (nconstrs,)
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gp_vars = [self.inner.getVarByName(var_name.decode()) for var_name in var_names]
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self.inner.addMConstr(constrs_lhs, gp_vars, constrs_sense, constrs_rhs)
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if stats is not None:
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if "Added constraints" not in stats:
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stats["Added constraints"] = 0
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stats["Added constraints"] += nconstrs
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def add_constr(self, constr: Any) -> None:
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if self.where == "lazy":
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self.inner.cbLazy(constr)
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elif self.where == "cut":
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self.inner.cbCut(constr)
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else:
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self.inner.addConstr(constr)
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def extract_after_load(self, h5: H5File) -> None:
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"""
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Given a model that has just been loaded, extracts static problem
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features, such as variable names and types, objective coefficients, etc.
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"""
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self.inner.update()
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self._extract_after_load_vars(h5)
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self._extract_after_load_constrs(h5)
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h5.put_scalar("static_sense", "min" if self.inner.modelSense > 0 else "max")
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h5.put_scalar("static_obj_offset", self.inner.objCon)
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def extract_after_lp(self, h5: H5File) -> None:
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"""
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Given a linear programming model that has just been solved, extracts
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dynamic problem features, such as optimal LP solution, basis status,
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etc.
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"""
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self._extract_after_lp_vars(h5)
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self._extract_after_lp_constrs(h5)
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h5.put_scalar("lp_obj_value", self.inner.objVal)
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h5.put_scalar("lp_wallclock_time", self.inner.runtime)
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def extract_after_mip(self, h5: H5File) -> None:
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"""
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Given a mixed-integer linear programming model that has just been
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solved, extracts dynamic problem features, such as optimal MIP solution.
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"""
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h5.put_scalar("mip_wallclock_time", self.inner.runtime)
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h5.put_scalar("mip_node_count", self.inner.nodeCount)
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if self.inner.status == GRB.INFEASIBLE:
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return
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gp_vars = self.inner.getVars()
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gp_constrs = self.inner.getConstrs()
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h5.put_array(
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"mip_var_values",
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np.array(self.inner.getAttr("x", gp_vars), dtype=float),
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)
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h5.put_array(
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"mip_constr_slacks",
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np.abs(np.array(self.inner.getAttr("slack", gp_constrs), dtype=float)),
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)
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h5.put_scalar("mip_obj_value", self.inner.objVal)
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h5.put_scalar("mip_obj_bound", self.inner.objBound)
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try:
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h5.put_scalar("mip_gap", self.inner.mipGap)
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except AttributeError:
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pass
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self._extract_after_mip_solution_pool(h5)
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def fix_variables(
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self,
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var_names: np.ndarray,
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var_values: np.ndarray,
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stats: Optional[Dict] = None,
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) -> None:
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assert len(var_values.shape) == 1
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assert len(var_values.shape) == 1
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assert var_names.shape == var_values.shape
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n_fixed = 0
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for (var_idx, var_name) in enumerate(var_names):
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var_val = var_values[var_idx]
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if np.isfinite(var_val):
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var = self.inner.getVarByName(var_name.decode())
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var.vtype = "C"
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var.lb = var_val
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var.ub = var_val
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n_fixed += 1
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if stats is not None:
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stats["Fixed variables"] = n_fixed
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def optimize(self) -> None:
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self.lazy_constrs_ = []
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def callback(m: gp.Model, where: int) -> None:
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assert self.lazy_separate is not None
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assert self.lazy_constrs_ is not None
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assert self.lazy_enforce is not None
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if where == GRB.Callback.MIPSOL:
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self.where = "lazy"
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violations = self.lazy_separate(self)
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self.lazy_constrs_.extend(violations)
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self.lazy_enforce(self, violations)
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self.where = "default"
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if self.lazy_enforce is not None:
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self.inner.Params.lazyConstraints = 1
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self.inner.optimize(callback)
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else:
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self.inner.optimize()
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def relax(self) -> "GurobiModel":
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return GurobiModel(self.inner.relax())
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def set_time_limit(self, time_limit_sec: float) -> None:
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self.inner.params.timeLimit = time_limit_sec
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def set_warm_starts(
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self,
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var_names: np.ndarray,
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var_values: np.ndarray,
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stats: Optional[Dict] = None,
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) -> None:
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assert len(var_values.shape) == 2
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(n_starts, n_vars) = var_values.shape
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assert len(var_names.shape) == 1
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assert var_names.shape[0] == n_vars
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self.inner.numStart = n_starts
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for start_idx in range(n_starts):
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self.inner.params.startNumber = start_idx
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for (var_idx, var_name) in enumerate(var_names):
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var_val = var_values[start_idx, var_idx]
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if np.isfinite(var_val):
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var = self.inner.getVarByName(var_name.decode())
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var.start = var_val
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if stats is not None:
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stats["WS: Count"] = n_starts
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stats["WS: Number of variables set"] = (
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np.isfinite(var_values).mean(axis=0).sum()
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)
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def _extract_after_load_vars(self, h5: H5File) -> None:
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gp_vars = self.inner.getVars()
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for (h5_field, gp_field) in {
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"static_var_names": "varName",
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"static_var_types": "vtype",
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}.items():
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h5.put_array(
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h5_field, np.array(self.inner.getAttr(gp_field, gp_vars), dtype="S")
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)
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for (h5_field, gp_field) in {
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"static_var_upper_bounds": "ub",
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"static_var_lower_bounds": "lb",
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"static_var_obj_coeffs": "obj",
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}.items():
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h5.put_array(
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h5_field, np.array(self.inner.getAttr(gp_field, gp_vars), dtype=float)
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)
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def _extract_after_load_constrs(self, h5: H5File) -> None:
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gp_constrs = self.inner.getConstrs()
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gp_vars = self.inner.getVars()
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rhs = np.array(self.inner.getAttr("rhs", gp_constrs), dtype=float)
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senses = np.array(self.inner.getAttr("sense", gp_constrs), dtype="S")
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names = np.array(self.inner.getAttr("constrName", gp_constrs), dtype="S")
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nrows, ncols = len(gp_constrs), len(gp_vars)
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tmp = lil_matrix((nrows, ncols), dtype=float)
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for (i, gp_constr) in enumerate(gp_constrs):
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expr = self.inner.getRow(gp_constr)
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for j in range(expr.size()):
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tmp[i, expr.getVar(j).index] = expr.getCoeff(j)
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lhs = tmp.tocoo()
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h5.put_array("static_constr_names", names)
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h5.put_array("static_constr_rhs", rhs)
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h5.put_array("static_constr_sense", senses)
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h5.put_sparse("static_constr_lhs", lhs)
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def _extract_after_lp_vars(self, h5: H5File) -> None:
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def _parse_gurobi_vbasis(b: int) -> str:
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if b == 0:
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return "B"
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elif b == -1:
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return "L"
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elif b == -2:
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return "U"
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elif b == -3:
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return "S"
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else:
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raise Exception(f"unknown vbasis: {b}")
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gp_vars = self.inner.getVars()
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h5.put_array(
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"lp_var_basis_status",
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np.array(
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[
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_parse_gurobi_vbasis(b)
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for b in self.inner.getAttr("vbasis", gp_vars)
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],
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dtype="S",
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),
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)
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for (h5_field, gp_field) in {
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"lp_var_reduced_costs": "rc",
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"lp_var_sa_obj_up": "saobjUp",
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"lp_var_sa_obj_down": "saobjLow",
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"lp_var_sa_ub_up": "saubUp",
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"lp_var_sa_ub_down": "saubLow",
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"lp_var_sa_lb_up": "salbUp",
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"lp_var_sa_lb_down": "salbLow",
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"lp_var_values": "x",
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}.items():
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h5.put_array(
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h5_field,
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np.array(self.inner.getAttr(gp_field, gp_vars), dtype=float),
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)
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def _extract_after_lp_constrs(self, h5: H5File) -> None:
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def _parse_gurobi_cbasis(v: int) -> str:
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if v == 0:
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return "B"
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if v == -1:
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return "N"
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raise Exception(f"unknown cbasis: {v}")
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gp_constrs = self.inner.getConstrs()
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h5.put_array(
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"lp_constr_basis_status",
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np.array(
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[
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_parse_gurobi_cbasis(c)
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for c in self.inner.getAttr("cbasis", gp_constrs)
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],
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dtype="S",
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),
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)
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for (h5_field, gp_field) in {
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"lp_constr_dual_values": "pi",
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"lp_constr_sa_rhs_up": "saRhsUp",
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"lp_constr_sa_rhs_down": "saRhsLow",
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}.items():
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h5.put_array(
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h5_field,
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np.array(self.inner.getAttr(gp_field, gp_constrs), dtype=float),
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)
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h5.put_array(
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"lp_constr_slacks",
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np.abs(np.array(self.inner.getAttr("slack", gp_constrs), dtype=float)),
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)
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def _extract_after_mip_solution_pool(self, h5: H5File) -> None:
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gp_vars = self.inner.getVars()
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pool_var_values = []
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pool_obj_values = []
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for i in range(self.inner.SolCount):
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self.inner.params.SolutionNumber = i
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try:
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pool_var_values.append(self.inner.getAttr("Xn", gp_vars))
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pool_obj_values.append(self.inner.PoolObjVal)
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except GurobiError:
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pass
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h5.put_array("pool_var_values", np.array(pool_var_values))
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h5.put_array("pool_obj_values", np.array(pool_obj_values))
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def write(self, filename: str) -> None:
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self.inner.update()
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self.inner.write(filename)
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