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85 lines
3.8 KiB
85 lines
3.8 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 parallel_solve(self, instances, n_jobs=1, n_trials=1):
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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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])
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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=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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nodes = results[i]["Solver"][0]["Nodes"]
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lb = results[i]["Problem"][0]["Lower bound"]
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ub = results[i]["Problem"][0]["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": i,
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"Wallclock Time": 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": nodes,
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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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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):
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for (name, solver) in self.solvers.items():
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solver.fit()
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