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Plot predicted objective value

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This commit is contained in:
Alinson S. Xavier
2020-02-26 15:45:02 -06:00
parent 32510d077d
commit 5eee00c626
8 changed files with 50 additions and 21 deletions
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46
benchmark/benchmark.py
View File

@@ -118,52 +118,72 @@ def charts():
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"
palette={
"baseline": "#9b59b6",
"ml-exact": "#3498db",
"ml-heuristic": "#95a5a6"
}
fig, axes = plt.subplots(nrows=1,
ncols=3,
figsize=(10,4),
gridspec_kw={'width_ratios': [3, 3, 2]},
)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
ncols=4,
figsize=(12,4),
gridspec_kw={'width_ratios': [2, 1, 1, 2]},
)
sns.stripplot(x="Solver",
y="Wallclock Time",
data=results,
ax=axes[0],
ax=ax1,
jitter=0.25,
palette=palette,
size=4.0,
);
sns.barplot(x="Solver",
y="Wallclock Time",
data=results,
ax=axes[0],
ax=ax1,
errwidth=0.,
alpha=0.3,
alpha=0.4,
palette=palette,
estimator=median,
);
axes[0].set(ylabel='Wallclock Time (s)')
axes[1].set_ylim(-0.5, 5.5)
ax1.set(ylabel='Wallclock Time (s)')
ax2.set_ylim(-0.5, 5.5)
sns.stripplot(x="Solver",
y="Gap (%)",
jitter=0.25,
data=results[results["Solver"] != "ml-heuristic"],
ax=axes[1],
ax=ax2,
palette=palette,
size=4.0,
);
axes[2].set_ylim(0.95,1.01)
ax3.set_ylim(0.95,1.05)
sns.stripplot(x="Solver",
y=primal_column,
jitter=0.25,
data=results[results["Solver"] == "ml-heuristic"],
ax=axes[2],
ax=ax3,
palette=palette,
);
sns.scatterplot(x=obj_column,
y=predicted_obj_column,
hue="Solver",
data=results[results["Solver"] == "ml-exact"],
ax=ax4,
palette=palette,
);
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("%s/performance.png" % basepath,
bbox_inches='tight',

4
miplearn/benchmark.py
View File

@@ -64,6 +64,8 @@ class BenchmarkRunner:
"Nodes",
"Mode",
"Sense",
"Predicted LB",
"Predicted UB",
])
lb = result["Lower bound"]
ub = result["Upper bound"]
@@ -78,6 +80,8 @@ class BenchmarkRunner:
"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")

2
miplearn/components/component.py
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@@ -15,7 +15,7 @@ class Component(ABC):
pass
@abstractmethod
def after_solve(self, solver, instance, model):
def after_solve(self, solver, instance, model, results):
pass
@abstractmethod

2
miplearn/components/lazy.py
View File

@@ -38,7 +38,7 @@ class LazyConstraintsComponent(Component):
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
def after_solve(self, solver, instance, model):
def after_solve(self, solver, instance, model, results):
pass
def fit(self, training_instances):

9
miplearn/components/objective.py
View File

@@ -27,8 +27,13 @@ class ObjectiveValueComponent(Component):
instance.predicted_lb = lb
logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
def after_solve(self, solver, instance, model):
pass
def after_solve(self, solver, instance, model, results):
if self.ub_regressor is not None:
results["Predicted UB"] = instance.predicted_ub
results["Predicted LB"] = instance.predicted_lb
else:
results["Predicted UB"] = None
results["Predicted LB"] = None
def fit(self, training_instances):
logger.debug("Extracting features...")

2
miplearn/components/primal.py
View File

@@ -135,7 +135,7 @@ class PrimalSolutionComponent(Component):
else:
solver.internal_solver.set_warm_start(solution)
def after_solve(self, solver, instance, model):
def after_solve(self, solver, instance, model, results):
pass
def fit(self, training_instances):

2
miplearn/solvers.py
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@@ -306,7 +306,7 @@ class LearningSolver:
logger.debug("Calling after_solve callbacks...")
for component in self.components.values():
component.after_solve(self, instance, model)
component.after_solve(self, instance, model, results)
# Store instance for future training
self.training_instances += [instance]

4
miplearn/tests/test_benchmark.py
View File

@@ -27,11 +27,11 @@ def test_benchmark():
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
assert benchmark.raw_results().values.shape == (12,13)
assert benchmark.raw_results().values.shape == (12,16)
benchmark.save_results("/tmp/benchmark.csv")
assert os.path.isfile("/tmp/benchmark.csv")
benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results("/tmp/benchmark.csv")
assert benchmark.raw_results().values.shape == (12,13)
assert benchmark.raw_results().values.shape == (12,16)