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93 lines
3.9 KiB
93 lines
3.9 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 .solvers import LearningSolver
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from copy import deepcopy
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import pandas as pd
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from tqdm.auto import tqdm
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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, fit=True, tee=False):
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for (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(results, solver=solver, name=name, instance=i)
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if fit:
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solver.fit()
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def parallel_solve(self, instances, n_jobs=1, n_trials=1):
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instances = instances * n_trials
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for (name, solver) in self.solvers.items():
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results = solver.parallel_solve(instances,
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n_jobs=n_jobs,
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label="Solve (%s)" % name,
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collect_training_data=False)
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for i in range(len(instances)):
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self._push_result(results[i], solver=solver, name=name, instance=i)
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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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self.results.to_csv(filename)
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def load_results(self, filename):
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self.results = pd.read_csv(filename, index_col=0)
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def load_state(self, filename):
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for (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 (name, solver) in self.solvers.items():
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solver.fit(training_instances)
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def _push_result(self, result, solver, name, instance):
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if self.results is None:
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self.results = pd.DataFrame(columns=["Solver",
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"Instance",
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"Wallclock Time",
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"Lower Bound",
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"Upper Bound",
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"Gap",
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"Nodes",
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"Mode",
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])
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lb = result["Lower bound"]
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ub = result["Upper bound"]
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gap = (ub - lb) / lb
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self.results = self.results.append({
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"Solver": name,
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"Instance": instance,
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"Wallclock Time": result["Wallclock time"],
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"Lower Bound": lb,
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"Upper Bound": ub,
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"Gap": gap,
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"Nodes": result["Nodes"],
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"Mode": solver.mode,
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}, ignore_index=True)
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groups = self.results.groupby("Instance")
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best_lower_bound = groups["Lower Bound"].transform("max")
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best_upper_bound = groups["Upper Bound"].transform("min")
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best_gap = groups["Gap"].transform("min")
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best_nodes = groups["Nodes"].transform("min")
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best_wallclock_time = groups["Wallclock Time"].transform("min")
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self.results["Relative Lower Bound"] = \
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self.results["Lower Bound"] / best_lower_bound
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self.results["Relative Upper Bound"] = \
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self.results["Upper Bound"] / best_upper_bound
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self.results["Relative Wallclock Time"] = \
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self.results["Wallclock Time"] / best_wallclock_time
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self.results["Relative Gap"] = \
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self.results["Gap"] / best_gap
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self.results["Relative Nodes"] = \
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self.results["Nodes"] / best_nodes
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