|
|
|
@ -76,6 +76,14 @@ class BenchmarkRunner:
|
|
|
|
|
for (solver_name, solver) in self.solvers.items():
|
|
|
|
|
solver.fit(training_instances)
|
|
|
|
|
|
|
|
|
|
def _compute_gap(self, ub, lb):
|
|
|
|
|
# solver did not find a solution and/or bound, use maximum gap possible
|
|
|
|
|
if lb is None or ub is None or lb * ub < 0:
|
|
|
|
|
return 1.0
|
|
|
|
|
else:
|
|
|
|
|
# divide by max(abs(ub),abs(lb)) to ensure gap <= 1
|
|
|
|
|
return (ub - lb) / max(abs(ub), abs(lb))
|
|
|
|
|
|
|
|
|
|
def _push_result(self, result, solver, solver_name, instance):
|
|
|
|
|
if self.results is None:
|
|
|
|
|
self.results = pd.DataFrame(
|
|
|
|
@ -85,12 +93,12 @@ class BenchmarkRunner:
|
|
|
|
|
"Instance",
|
|
|
|
|
]
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
lb = result["Lower bound"]
|
|
|
|
|
ub = result["Upper bound"]
|
|
|
|
|
result["Solver"] = solver_name
|
|
|
|
|
result["Instance"] = instance
|
|
|
|
|
result["Gap"] = (ub - lb) / lb
|
|
|
|
|
result["Gap"] = self._compute_gap(
|
|
|
|
|
ub=result["Lower bound"],
|
|
|
|
|
lb=result["Upper bound"],
|
|
|
|
|
)
|
|
|
|
|
result["Mode"] = solver.mode
|
|
|
|
|
self.results = self.results.append(pd.DataFrame([result]))
|
|
|
|
|
|
|
|
|
|