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Benchmark: Add extra columns to CSV

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This commit is contained in:
Alinson S. Xavier
2021-01-12 11:22:42 -06:00
parent f77d1d5de9
commit c9ad7a3f56
5 changed files with 60 additions and 51 deletions
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73
miplearn/benchmark.py
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@@ -77,56 +77,37 @@ class BenchmarkRunner:
def _push_result(self, result, solver, solver_name, instance):
if self.results is None:
self.results = pd.DataFrame(
# Show the following columns first in the CSV file
columns=[
"Solver",
"Instance",
"Wallclock Time",
"Lower Bound",
"Upper Bound",
"Gap",
"Nodes",
"Mode",
"Sense",
"Predicted LB",
"Predicted UB",
]
)
lb = result["Lower bound"]
ub = result["Upper bound"]
gap = (ub - lb) / lb
if "Predicted LB" not in result:
result["Predicted LB"] = float("nan")
result["Predicted UB"] = float("nan")
self.results = self.results.append(
{
"Solver": solver_name,
"Instance": instance,
"Wallclock Time": result["Wallclock time"],
"Lower Bound": lb,
"Upper Bound": ub,
"Gap": gap,
"Nodes": result["Nodes"],
"Mode": solver.mode,
"Sense": result["Sense"],
"Predicted LB": result["Predicted LB"],
"Predicted UB": result["Predicted UB"],
},
ignore_index=True,
)
result["Solver"] = solver_name
result["Instance"] = instance
result["Gap"] = (ub - lb) / lb
result["Mode"] = solver.mode
del result["Log"]
self.results = self.results.append(pd.DataFrame([result]))
# Compute relative statistics
groups = self.results.groupby("Instance")
best_lower_bound = groups["Lower Bound"].transform("max")
best_upper_bound = groups["Upper Bound"].transform("min")
best_lower_bound = groups["Lower bound"].transform("max")
best_upper_bound = groups["Upper bound"].transform("min")
best_gap = groups["Gap"].transform("min")
best_nodes = np.maximum(1, groups["Nodes"].transform("min"))
best_wallclock_time = groups["Wallclock Time"].transform("min")
self.results["Relative Lower Bound"] = (
self.results["Lower Bound"] / best_lower_bound
best_wallclock_time = groups["Wallclock time"].transform("min")
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 upper bound"] = (
self.results["Upper bound"] / best_upper_bound
)
self.results["Relative Wallclock Time"] = (
self.results["Wallclock Time"] / best_wallclock_time
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
@@ -143,12 +124,12 @@ class BenchmarkRunner:
sense = results.loc[0, "Sense"]
if sense == "min":
primal_column = "Relative Upper Bound"
obj_column = "Upper Bound"
primal_column = "Relative upper bound"
obj_column = "Upper bound"
predicted_obj_column = "Predicted UB"
else:
primal_column = "Relative Lower Bound"
obj_column = "Lower Bound"
primal_column = "Relative lower bound"
obj_column = "Lower bound"
predicted_obj_column = "Predicted LB"
fig, (ax1, ax2, ax3, ax4) = plt.subplots(
@@ -158,10 +139,10 @@ class BenchmarkRunner:
gridspec_kw={"width_ratios": [2, 1, 1, 2]},
)
# Figure 1: Solver x Wallclock Time
# Figure 1: Solver x Wallclock time
sns.stripplot(
x="Solver",
y="Wallclock Time",
y="Wallclock time",
data=results,
ax=ax1,
jitter=0.25,
@@ -169,14 +150,14 @@ class BenchmarkRunner:
)
sns.barplot(
x="Solver",
y="Wallclock Time",
y="Wallclock time",
data=results,
ax=ax1,
errwidth=0.0,
alpha=0.4,
estimator=median,
)
ax1.set(ylabel="Wallclock Time (s)")
ax1.set(ylabel="Wallclock time (s)")
# Figure 2: Solver x Gap (%)
ax2.set_ylim(-0.5, 5.5)