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132 lines
5.0 KiB
132 lines
5.0 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 dill
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import pickle
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import tempfile
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import os
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from miplearn.instance 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.learning import LearningSolver
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from . import _get_knapsack_instance, get_internal_solvers
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logger = logging.getLogger(__name__)
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def test_learning_solver():
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for mode in ["exact", "heuristic"]:
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for internal_solver in get_internal_solvers():
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logger.info("Solver: %s" % internal_solver)
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instance = _get_knapsack_instance(internal_solver)
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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 hasattr(instance, "features")
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sample = instance.training_data[0]
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assert sample.solution["x"][0] == 1.0
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assert sample.solution["x"][1] == 0.0
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assert sample.solution["x"][2] == 1.0
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assert sample.solution["x"][3] == 1.0
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assert sample.lower_bound == 1183.0
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assert sample.upper_bound == 1183.0
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assert round(sample.lp_solution["x"][0], 3) == 1.000
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assert round(sample.lp_solution["x"][1], 3) == 0.923
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assert round(sample.lp_solution["x"][2], 3) == 1.000
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assert round(sample.lp_solution["x"][3], 3) == 0.000
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assert round(sample.lp_value, 3) == 1287.923
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assert len(sample.mip_log) > 100
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solver.fit([instance])
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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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for internal_solver in get_internal_solvers():
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logger.info("Solver: %s" % internal_solver)
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instance = _get_knapsack_instance(internal_solver)
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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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for internal_solver in get_internal_solvers():
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instances = [_get_knapsack_instance(internal_solver) 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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data = instance.training_data[0]
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assert len(data.solution["x"].keys()) == 4
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def test_solve_fit_from_disk():
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for internal_solver in get_internal_solvers():
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# Create instances and pickle them
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instances = []
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for k in range(3):
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instance = _get_knapsack_instance(internal_solver)
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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(instances[0].filename)
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assert len(instance_loaded.training_data) > 0
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assert instance_loaded.features.instance is not None
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assert instance_loaded.features.variables is not None
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assert instance_loaded.features.constraints is not None
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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(instance.filename)
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assert len(instance_loaded.training_data) > 0
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assert instance_loaded.features.instance is not None
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assert instance_loaded.features.variables is not None
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assert instance_loaded.features.constraints is not None
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# Delete temporary files
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for instance in instances:
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os.remove(instance.filename)
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def test_simulate_perfect():
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internal_solver = GurobiSolver
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instance = _get_knapsack_instance(internal_solver)
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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["Lower bound"] == stats["Objective: Predicted lower bound"]
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def test_gap():
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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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