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Minor fixed to BenchmarkRunner

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This commit is contained in:
Alinson S. Xavier
2020-10-05 11:25:53 -05:00
parent a221740ac5
commit 75570ceaeb
1 changed files with 43 additions and 22 deletions
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65
miplearn/benchmark.py
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@@ -5,6 +5,8 @@
from copy import deepcopy
import pandas as pd
import numpy as np
import logging
from tqdm.auto import tqdm
from .solvers.learning import LearningSolver
@@ -19,22 +21,30 @@ class BenchmarkRunner:
self.results = None
def solve(self, instances, tee=False):
for (name, solver) in self.solvers.items():
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, name=name, instance=i)
self._push_result(results, solver=solver, solver_name=solver_name, instance=i)
def parallel_solve(self, instances, n_jobs=1, n_trials=1):
instances = instances * n_trials
for (name, solver) in self.solvers.items():
results = solver.parallel_solve(instances,
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)" % name)
for i in range(len(instances)):
label="Solve (%s)" % solver_name)
for i in range(len(trials)):
idx = (i % len(instances)) + index_offset
self._push_result(results[i],
solver=solver,
name=name,
instance=i)
solver_name=solver_name,
instance=idx)
self._restore_miplearn_logger()
def raw_results(self):
return self.results
@@ -46,14 +56,14 @@ class BenchmarkRunner:
self.results = pd.read_csv(filename, index_col=0)
def load_state(self, filename):
for (name, solver) in self.solvers.items():
for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)
def fit(self, training_instances):
for (name, solver) in self.solvers.items():
for (solver_name, solver) in self.solvers.items():
solver.fit(training_instances)
def _push_result(self, result, solver, name, instance):
def _push_result(self, result, solver, solver_name, instance):
if self.results is None:
self.results = pd.DataFrame(columns=["Solver",
"Instance",
@@ -74,7 +84,7 @@ class BenchmarkRunner:
result["Predicted LB"] = float("nan")
result["Predicted UB"] = float("nan")
self.results = self.results.append({
"Solver": name,
"Solver": solver_name,
"Instance": instance,
"Wallclock Time": result["Wallclock time"],
"Lower Bound": lb,
@@ -90,7 +100,7 @@ class BenchmarkRunner:
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_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
@@ -135,7 +145,7 @@ class BenchmarkRunner:
ax=ax1,
jitter=0.25,
size=4.0,
);
)
sns.barplot(x="Solver",
y="Wallclock Time",
data=results,
@@ -143,7 +153,7 @@ class BenchmarkRunner:
errwidth=0.,
alpha=0.4,
estimator=median,
);
)
ax1.set(ylabel='Wallclock Time (s)')
# Figure 2: Solver x Gap (%)
@@ -154,7 +164,7 @@ class BenchmarkRunner:
data=results[results["Mode"] != "heuristic"],
ax=ax2,
size=4.0,
);
)
# Figure 3: Solver x Primal Value
ax3.set_ylim(0.95,1.05)
@@ -163,7 +173,7 @@ class BenchmarkRunner:
jitter=0.25,
data=results[results["Mode"] == "heuristic"],
ax=ax3,
);
)
# Figure 4: Predicted vs Actual Objective Value
sns.scatterplot(x=obj_column,
@@ -171,12 +181,23 @@ class BenchmarkRunner:
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.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)
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)