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114 lines
3.7 KiB
114 lines
3.7 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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 copy import deepcopy
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import pandas as pd
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import numpy as np
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import logging
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from tqdm.auto import tqdm
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import os
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from .solvers.learning import LearningSolver
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class BenchmarkRunner:
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def __init__(self, solvers):
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assert isinstance(solvers, dict)
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for solver in solvers.values():
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assert isinstance(solver, LearningSolver)
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self.solvers = solvers
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self.results = None
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def solve(self, instances, tee=False):
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for (solver_name, solver) in self.solvers.items():
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for i in tqdm(range(len((instances)))):
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results = solver.solve(deepcopy(instances[i]), tee=tee)
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self._push_result(
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results,
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solver=solver,
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solver_name=solver_name,
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instance=i,
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)
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def parallel_solve(
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self,
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instances,
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n_jobs=1,
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n_trials=1,
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index_offset=0,
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):
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self._silence_miplearn_logger()
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trials = instances * n_trials
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for (solver_name, solver) in self.solvers.items():
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results = solver.parallel_solve(
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trials,
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n_jobs=n_jobs,
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label="Solve (%s)" % solver_name,
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output=None,
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)
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for i in range(len(trials)):
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idx = (i % len(instances)) + index_offset
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self._push_result(
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results[i],
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solver=solver,
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solver_name=solver_name,
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instance=idx,
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)
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self._restore_miplearn_logger()
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def raw_results(self):
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return self.results
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def save_results(self, filename):
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os.makedirs(os.path.dirname(filename), exist_ok=True)
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self.results.to_csv(filename)
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def load_results(self, filename):
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self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
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def load_state(self, filename):
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for (solver_name, solver) in self.solvers.items():
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solver.load_state(filename)
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def fit(self, training_instances):
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for (solver_name, solver) in self.solvers.items():
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solver.fit(training_instances)
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@staticmethod
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def _compute_gap(ub, lb):
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# solver did not find a solution and/or bound, use maximum gap possible
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if lb is None or ub is None or lb * ub < 0:
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return 1.0
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else:
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# divide by max(abs(ub),abs(lb)) to ensure gap <= 1
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return (ub - lb) / max(abs(ub), abs(lb))
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def _push_result(self, result, solver, solver_name, instance):
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if self.results is None:
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self.results = pd.DataFrame(
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# Show the following columns first in the CSV file
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columns=[
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"Solver",
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"Instance",
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]
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)
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result["Solver"] = solver_name
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result["Instance"] = instance
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result["Gap"] = self._compute_gap(
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ub=result["Lower bound"],
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lb=result["Upper bound"],
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)
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result["Mode"] = solver.mode
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self.results = self.results.append(pd.DataFrame([result]))
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def _silence_miplearn_logger(self):
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miplearn_logger = logging.getLogger("miplearn")
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self.prev_log_level = miplearn_logger.getEffectiveLevel()
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miplearn_logger.setLevel(logging.WARNING)
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def _restore_miplearn_logger(self):
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miplearn_logger = logging.getLogger("miplearn")
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miplearn_logger.setLevel(self.prev_log_level)
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