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https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 01:18:52 -06:00
Fix some _compute_gap corner cases; add tests
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@@ -78,9 +78,12 @@ class BenchmarkRunner:
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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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# solver did not find a solution and/or bound, use maximum gap possible
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return 1.0
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elif abs(ub - lb) < 1e-6:
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# avoid division by zero when ub = lb = 0
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return 0.0
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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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@@ -97,8 +100,8 @@ class BenchmarkRunner:
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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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ub=result["Upper bound"],
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lb=result["Lower 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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@@ -35,3 +35,13 @@ def test_benchmark():
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benchmark = BenchmarkRunner(test_solvers)
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benchmark.load_results("/tmp/benchmark.csv")
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assert benchmark.raw_results().values.shape == (12, 14)
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def test_gap():
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assert BenchmarkRunner._compute_gap(ub=0.0, lb=0.0) == 0.0
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assert BenchmarkRunner._compute_gap(ub=1.0, lb=0.5) == 0.5
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assert BenchmarkRunner._compute_gap(ub=1.0, lb=1.0) == 0.0
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assert BenchmarkRunner._compute_gap(ub=1.0, lb=-1.0) == 1.0
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assert BenchmarkRunner._compute_gap(ub=1.0, lb=None) == 1.0
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assert BenchmarkRunner._compute_gap(ub=None, lb=1.0) == 1.0
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assert BenchmarkRunner._compute_gap(ub=None, lb=None) == 1.0
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