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45 lines
1.9 KiB
45 lines
1.9 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 .solvers import LearningSolver
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import pandas as pd
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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 load_fit(self, filename):
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for (name, solver) in self.solvers.items():
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solver.load(filename)
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solver.fit()
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def parallel_solve(self, instances, n_jobs=1):
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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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"Optimal Value",
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])
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for (name, solver) in self.solvers.items():
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results = solver.parallel_solve(instances, n_jobs=n_jobs, label=name)
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for i in range(len(instances)):
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wallclock_time = None
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for key in ["Time", "Wall time", "Wallclock time"]:
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if key not in results[i]["Solver"][0].keys():
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continue
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if str(results[i]["Solver"][0][key]) == "<undefined>":
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continue
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wallclock_time = float(results[i]["Solver"][0][key])
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self.results = self.results.append({
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"Solver": name,
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"Instance": i,
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"Wallclock Time": wallclock_time,
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"Optimal Value": results[i]["Problem"][0]["Lower bound"]
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}, ignore_index=True)
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def raw_results(self):
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return self.results |