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96 lines
2.9 KiB
96 lines
2.9 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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from typing import Any, FrozenSet, List
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import gurobipy as gp
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import networkx as nx
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import pytest
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from gurobipy import GRB
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from networkx import Graph
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from overrides import overrides
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from miplearn.solvers.learning import InternalSolver
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from miplearn.components.dynamic_user_cuts import UserCutsComponent
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from miplearn.instance.base import Instance
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.learning import LearningSolver
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logger = logging.getLogger(__name__)
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class GurobiStableSetProblem(Instance):
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def __init__(self, graph: Graph) -> None:
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super().__init__()
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self.graph: Graph = graph
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@overrides
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def to_model(self) -> Any:
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model = gp.Model()
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x = [model.addVar(vtype=GRB.BINARY) for _ in range(len(self.graph.nodes))]
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model.setObjective(gp.quicksum(x), GRB.MAXIMIZE)
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for e in list(self.graph.edges):
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model.addConstr(x[e[0]] + x[e[1]] <= 1)
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return model
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@overrides
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def has_user_cuts(self) -> bool:
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return True
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@overrides
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def find_violated_user_cuts(self, model: Any) -> List[str]:
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assert isinstance(model, gp.Model)
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vals = model.cbGetNodeRel(model.getVars())
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violations = []
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for clique in nx.find_cliques(self.graph):
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if sum(vals[i] for i in clique) > 1:
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violations.append(",".join([str(i) for i in clique]))
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return violations
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@overrides
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def enforce_user_cut(
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self,
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solver: InternalSolver,
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model: Any,
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cid: str,
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) -> Any:
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clique = [int(i) for i in cid.split(",")]
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x = model.getVars()
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model.addConstr(gp.quicksum([x[i] for i in clique]) <= 1)
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@pytest.fixture
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def stab_instance() -> Instance:
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graph = nx.generators.random_graphs.binomial_graph(50, 0.50, seed=42)
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return GurobiStableSetProblem(graph)
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@pytest.fixture
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def solver() -> LearningSolver:
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return LearningSolver(
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solver=GurobiSolver(),
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components=[UserCutsComponent()],
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)
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def test_usage(
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stab_instance: Instance,
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solver: LearningSolver,
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) -> None:
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stats_before = solver.solve(stab_instance)
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sample = stab_instance.get_samples()[0]
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user_cuts_enforced = sample.get_set("mip_user_cuts_enforced")
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assert user_cuts_enforced is not None
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assert len(user_cuts_enforced) > 0
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assert stats_before["UserCuts: Added ahead-of-time"] == 0
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assert stats_before["UserCuts: Added in callback"] > 0
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solver.fit([stab_instance])
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stats_after = solver.solve(stab_instance)
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assert (
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stats_after["UserCuts: Added ahead-of-time"]
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== stats_before["UserCuts: Added in callback"]
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)
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