ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-08 18:38:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Add customizable branch priority; add more metrics to BenchmarkRunner

Browse Source
This commit is contained in:
Alinson S. Xavier
2020-01-28 06:51:49 -06:00
parent 99ac7aa718
commit f7d20ed52b
4 changed files with 70 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

36
miplearn/benchmark.py
View File

@@ -18,15 +18,21 @@ class BenchmarkRunner:
solver.load(filename)
solver.fit()
def parallel_solve(self, instances, n_jobs=1):
def parallel_solve(self, instances, n_jobs=1, n_trials=1):
if self.results is None:
self.results = pd.DataFrame(columns=["Solver",
"Instance",
"Wallclock Time",
"Obj Value",
"Lower Bound",
"Upper Bound",
"Gap",
"Nodes",
])
instances = instances * n_trials
for (name, solver) in self.solvers.items():
results = solver.parallel_solve(instances, n_jobs=n_jobs, label=name)
results = solver.parallel_solve(instances,
n_jobs=n_jobs,
label=name)
for i in range(len(instances)):
wallclock_time = None
for key in ["Time", "Wall time", "Wallclock time"]:
@@ -35,19 +41,35 @@ class BenchmarkRunner:
if str(results[i]["Solver"][0][key]) == "<undefined>":
continue
wallclock_time = float(results[i]["Solver"][0][key])
nodes = results[i]["Solver"][0]["Nodes"]
lb = results[i]["Problem"][0]["Lower bound"]
ub = results[i]["Problem"][0]["Upper bound"]
gap = (ub - lb) / lb
self.results = self.results.append({
"Solver": name,
"Instance": i,
"Wallclock Time": wallclock_time,
"Obj Value": results[i]["Problem"][0]["Lower bound"]
"Lower Bound": lb,
"Upper Bound": ub,
"Gap": gap,
"Nodes": nodes,
}, ignore_index=True)
groups = self.results.groupby("Instance")
best_obj_value = groups["Obj Value"].transform("max")
best_lower_bound = groups["Lower Bound"].transform("max")
best_upper_bound = groups["Upper Bound"].transform("min")
best_gap = groups["Gap"].transform("min")
best_nodes = groups["Nodes"].transform("min")
best_wallclock_time = groups["Wallclock Time"].transform("min")
self.results["Relative Obj Value"] = \
self.results["Obj Value"] / best_obj_value
self.results["Relative Lower Bound"] = \
self.results["Lower Bound"] / best_lower_bound
self.results["Relative Upper Bound"] = \
self.results["Upper Bound"] / best_upper_bound
self.results["Relative Wallclock Time"] = \
self.results["Wallclock Time"] / best_wallclock_time
self.results["Relative Gap"] = \
self.results["Gap"] / best_gap
self.results["Relative Nodes"] = \
self.results["Nodes"] / best_nodes
def raw_results(self):
return self.results