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132 lines
4.6 KiB
132 lines
4.6 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from .transformers import PerVariableTransformer
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from .warmstart import WarmStartComponent
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from .branching import BranchPriorityComponent
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import pyomo.environ as pe
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import numpy as np
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from copy import deepcopy
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import pickle
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from tqdm import tqdm
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from joblib import Parallel, delayed
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from scipy.stats import randint
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import multiprocessing
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def _gurobi_factory():
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solver = pe.SolverFactory('gurobi_persistent')
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solver.options["threads"] = 4
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solver.options["Seed"] = randint(low=0, high=1000).rvs()
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return solver
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class LearningSolver:
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"""
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Mixed-Integer Linear Programming (MIP) solver that extracts information from previous runs,
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using Machine Learning methods, to accelerate the solution of new (yet unseen) instances.
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"""
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def __init__(self,
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threads=4,
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internal_solver_factory=_gurobi_factory,
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components=None,
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mode=None):
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self.is_persistent = None
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self.internal_solver = None
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self.components = components
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self.internal_solver_factory = internal_solver_factory
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if self.components is not None:
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assert isinstance(self.components, dict)
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else:
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self.components = {
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"warm-start": WarmStartComponent(),
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#"branch-priority": BranchPriorityComponent(),
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}
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if mode is not None:
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assert mode in ["exact", "heuristic"]
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for component in self.components.values():
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component.mode = mode
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def _create_solver(self):
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self.internal_solver = self.internal_solver_factory()
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self.is_persistent = hasattr(self.internal_solver, "set_instance")
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def solve(self, instance, tee=False):
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model = instance.to_model()
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self._create_solver()
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if self.is_persistent:
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self.internal_solver.set_instance(model)
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for component in self.components.values():
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component.before_solve(self, instance, model)
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if self.is_persistent:
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solve_results = self.internal_solver.solve(tee=tee, warmstart=True)
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else:
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solve_results = self.internal_solver.solve(model, tee=tee, warmstart=True)
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solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
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for component in self.components.values():
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component.after_solve(self, instance, model)
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return solve_results
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def parallel_solve(self,
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instances,
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n_jobs=4,
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label="Solve",
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collect_training_data=True,
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):
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self.internal_solver = None
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def _process(instance):
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solver = deepcopy(self)
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results = solver.solve(instance)
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solver.internal_solver = None
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if not collect_training_data:
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solver.components = {}
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return solver, results
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solver_result_pairs = Parallel(n_jobs=n_jobs)(
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delayed(_process)(instance)
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for instance in tqdm(instances, desc=label, ncols=80)
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)
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subsolvers = [p[0] for p in solver_result_pairs]
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results = [p[1] for p in solver_result_pairs]
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for (name, component) in self.components.items():
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subcomponents = [subsolver.components[name]
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for subsolver in subsolvers
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if name in subsolver.components.keys()]
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self.components[name].merge(subcomponents)
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return results
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def fit(self):
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for component in self.components.values():
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component.fit(self)
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def save_state(self, filename):
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with open(filename, "wb") as file:
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pickle.dump({
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"version": 2,
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"components": self.components,
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}, file)
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def load_state(self, filename):
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with open(filename, "rb") as file:
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data = pickle.load(file)
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assert data["version"] == 2
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for (component_name, component) in data["components"].items():
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if component_name not in self.components.keys():
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continue
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else:
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self.components[component_name].merge([component])
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