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146 lines
5.4 KiB
146 lines
5.4 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 os
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import tempfile
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from typing import List, cast
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import dill
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from miplearn.instance.base import Instance
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from miplearn.instance.picklegz import PickleGzInstance, write_pickle_gz, read_pickle_gz
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.internal import InternalSolver
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from miplearn.solvers.learning import LearningSolver
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# noinspection PyUnresolvedReferences
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from tests.solvers.test_internal_solver import internal_solvers
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from miplearn.solvers.tests import assert_equals
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logger = logging.getLogger(__name__)
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def test_learning_solver(
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internal_solvers: List[InternalSolver],
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) -> None:
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for mode in ["exact", "heuristic"]:
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for internal_solver in internal_solvers:
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logger.info("Solver: %s" % internal_solver)
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instance = internal_solver.build_test_instance_knapsack()
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solver = LearningSolver(
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solver=internal_solver,
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mode=mode,
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)
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solver.solve(instance)
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assert len(instance.samples) > 0
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sample = instance.samples[0]
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after_mip = sample.after_mip
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assert after_mip is not None
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assert after_mip.variables is not None
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assert after_mip.variables.values == [1.0, 0.0, 1.0, 1.0, 61.0]
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assert after_mip.mip_solve is not None
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assert after_mip.mip_solve.mip_lower_bound == 1183.0
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assert after_mip.mip_solve.mip_upper_bound == 1183.0
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assert after_mip.mip_solve.mip_log is not None
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assert len(after_mip.mip_solve.mip_log) > 100
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after_lp = sample.after_lp
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assert after_lp is not None
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assert after_lp.variables is not None
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assert_equals(after_lp.variables.values, [1.0, 0.923077, 1.0, 0.0, 67.0])
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assert after_lp.lp_solve is not None
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assert after_lp.lp_solve.lp_value is not None
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assert round(after_lp.lp_solve.lp_value, 3) == 1287.923
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assert after_lp.lp_solve.lp_log is not None
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assert len(after_lp.lp_solve.lp_log) > 100
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solver.fit([instance], n_jobs=4)
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solver.solve(instance)
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# Assert solver is picklable
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with tempfile.TemporaryFile() as file:
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dill.dump(solver, file)
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def test_solve_without_lp(
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internal_solvers: List[InternalSolver],
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) -> None:
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for internal_solver in internal_solvers:
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logger.info("Solver: %s" % internal_solver)
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instance = internal_solver.build_test_instance_knapsack()
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solver = LearningSolver(
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solver=internal_solver,
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solve_lp=False,
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)
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solver.solve(instance)
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solver.fit([instance])
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solver.solve(instance)
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def test_parallel_solve(
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internal_solvers: List[InternalSolver],
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) -> None:
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for internal_solver in internal_solvers:
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instances = [internal_solver.build_test_instance_knapsack() for _ in range(10)]
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solver = LearningSolver(solver=internal_solver)
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results = solver.parallel_solve(instances, n_jobs=3)
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assert len(results) == 10
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for instance in instances:
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assert len(instance.samples) == 1
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def test_solve_fit_from_disk(
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internal_solvers: List[InternalSolver],
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) -> None:
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for internal_solver in internal_solvers:
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# Create instances and pickle them
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instances: List[Instance] = []
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for k in range(3):
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instance = internal_solver.build_test_instance_knapsack()
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with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
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instances += [PickleGzInstance(file.name)]
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write_pickle_gz(instance, file.name)
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# Test: solve
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solver = LearningSolver(solver=internal_solver)
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solver.solve(instances[0])
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instance_loaded = read_pickle_gz(cast(PickleGzInstance, instances[0]).filename)
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assert len(instance_loaded.samples) > 0
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# Test: parallel_solve
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solver.parallel_solve(instances)
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for instance in instances:
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instance_loaded = read_pickle_gz(cast(PickleGzInstance, instance).filename)
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assert len(instance_loaded.samples) > 0
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# Delete temporary files
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for instance in instances:
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os.remove(cast(PickleGzInstance, instance).filename)
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def test_simulate_perfect() -> None:
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internal_solver = GurobiSolver()
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instance = internal_solver.build_test_instance_knapsack()
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with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
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write_pickle_gz(instance, tmp.name)
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solver = LearningSolver(
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solver=internal_solver,
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simulate_perfect=True,
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)
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stats = solver.solve(PickleGzInstance(tmp.name))
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assert stats["mip_lower_bound"] == stats["Objective: Predicted lower bound"]
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def test_gap() -> None:
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assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
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assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
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assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
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assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
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assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
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assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
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assert LearningSolver._compute_gap(ub=None, lb=None) is None
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