You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
227 lines
7.2 KiB
227 lines
7.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
|
# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
|
from copy import deepcopy
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
import logging
|
|
from tqdm.auto import tqdm
|
|
|
|
from .solvers.learning import LearningSolver
|
|
|
|
|
|
class BenchmarkRunner:
|
|
def __init__(self, solvers):
|
|
assert isinstance(solvers, dict)
|
|
for solver in solvers.values():
|
|
assert isinstance(solver, LearningSolver)
|
|
self.solvers = solvers
|
|
self.results = None
|
|
|
|
def solve(self, instances, tee=False):
|
|
for (solver_name, solver) in self.solvers.items():
|
|
for i in tqdm(range(len((instances)))):
|
|
results = solver.solve(deepcopy(instances[i]), tee=tee)
|
|
self._push_result(
|
|
results,
|
|
solver=solver,
|
|
solver_name=solver_name,
|
|
instance=i,
|
|
)
|
|
|
|
def parallel_solve(
|
|
self,
|
|
instances,
|
|
n_jobs=1,
|
|
n_trials=1,
|
|
index_offset=0,
|
|
):
|
|
self._silence_miplearn_logger()
|
|
trials = instances * n_trials
|
|
for (solver_name, solver) in self.solvers.items():
|
|
results = solver.parallel_solve(
|
|
trials,
|
|
n_jobs=n_jobs,
|
|
label="Solve (%s)" % solver_name,
|
|
output=None,
|
|
)
|
|
for i in range(len(trials)):
|
|
idx = (i % len(instances)) + index_offset
|
|
self._push_result(
|
|
results[i],
|
|
solver=solver,
|
|
solver_name=solver_name,
|
|
instance=idx,
|
|
)
|
|
self._restore_miplearn_logger()
|
|
|
|
def raw_results(self):
|
|
return self.results
|
|
|
|
def save_results(self, filename):
|
|
self.results.to_csv(filename)
|
|
|
|
def load_results(self, filename):
|
|
self.results = pd.read_csv(filename, index_col=0)
|
|
|
|
def load_state(self, filename):
|
|
for (solver_name, solver) in self.solvers.items():
|
|
solver.load_state(filename)
|
|
|
|
def fit(self, training_instances):
|
|
for (solver_name, solver) in self.solvers.items():
|
|
solver.fit(training_instances)
|
|
|
|
def _push_result(self, result, solver, solver_name, instance):
|
|
if self.results is None:
|
|
self.results = pd.DataFrame(
|
|
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,
|
|
)
|
|
groups = self.results.groupby("Instance")
|
|
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
|
|
)
|
|
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 save_chart(self, filename):
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
from numpy import median
|
|
|
|
sns.set_style("whitegrid")
|
|
sns.set_palette("Blues_r")
|
|
results = self.raw_results()
|
|
results["Gap (%)"] = results["Gap"] * 100.0
|
|
|
|
sense = results.loc[0, "Sense"]
|
|
if sense == "min":
|
|
primal_column = "Relative Upper Bound"
|
|
obj_column = "Upper Bound"
|
|
predicted_obj_column = "Predicted UB"
|
|
else:
|
|
primal_column = "Relative Lower Bound"
|
|
obj_column = "Lower Bound"
|
|
predicted_obj_column = "Predicted LB"
|
|
|
|
fig, (ax1, ax2, ax3, ax4) = plt.subplots(
|
|
nrows=1,
|
|
ncols=4,
|
|
figsize=(12, 4),
|
|
gridspec_kw={"width_ratios": [2, 1, 1, 2]},
|
|
)
|
|
|
|
# Figure 1: Solver x Wallclock Time
|
|
sns.stripplot(
|
|
x="Solver",
|
|
y="Wallclock Time",
|
|
data=results,
|
|
ax=ax1,
|
|
jitter=0.25,
|
|
size=4.0,
|
|
)
|
|
sns.barplot(
|
|
x="Solver",
|
|
y="Wallclock Time",
|
|
data=results,
|
|
ax=ax1,
|
|
errwidth=0.0,
|
|
alpha=0.4,
|
|
estimator=median,
|
|
)
|
|
ax1.set(ylabel="Wallclock Time (s)")
|
|
|
|
# Figure 2: Solver x Gap (%)
|
|
ax2.set_ylim(-0.5, 5.5)
|
|
sns.stripplot(
|
|
x="Solver",
|
|
y="Gap (%)",
|
|
jitter=0.25,
|
|
data=results[results["Mode"] != "heuristic"],
|
|
ax=ax2,
|
|
size=4.0,
|
|
)
|
|
|
|
# Figure 3: Solver x Primal Value
|
|
ax3.set_ylim(0.95, 1.05)
|
|
sns.stripplot(
|
|
x="Solver",
|
|
y=primal_column,
|
|
jitter=0.25,
|
|
data=results[results["Mode"] == "heuristic"],
|
|
ax=ax3,
|
|
)
|
|
|
|
# Figure 4: Predicted vs Actual Objective Value
|
|
sns.scatterplot(
|
|
x=obj_column,
|
|
y=predicted_obj_column,
|
|
hue="Solver",
|
|
data=results[results["Mode"] != "heuristic"],
|
|
ax=ax4,
|
|
)
|
|
xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
|
|
ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls="-", color="#cccccc")
|
|
ax4.set_xlim(xlim)
|
|
ax4.set_ylim(ylim)
|
|
ax4.get_legend().remove()
|
|
|
|
fig.tight_layout()
|
|
plt.savefig(filename, bbox_inches="tight", dpi=150)
|
|
|
|
def _silence_miplearn_logger(self):
|
|
miplearn_logger = logging.getLogger("miplearn")
|
|
self.prev_log_level = miplearn_logger.getEffectiveLevel()
|
|
miplearn_logger.setLevel(logging.WARNING)
|
|
|
|
def _restore_miplearn_logger(self):
|
|
miplearn_logger = logging.getLogger("miplearn")
|
|
miplearn_logger.setLevel(self.prev_log_level)
|