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

4
miplearn/benchmark.py
View File

@@ -64,6 +64,8 @@ class BenchmarkRunner:
"Nodes", "Nodes",
"Mode", "Mode",
"Sense", "Sense",
"Predicted LB",
"Predicted UB",
]) ])
lb = result["Lower bound"] lb = result["Lower bound"]
ub = result["Upper bound"] ub = result["Upper bound"]
@@ -78,6 +80,8 @@ class BenchmarkRunner:
"Nodes": result["Nodes"], "Nodes": result["Nodes"],
"Mode": solver.mode, "Mode": solver.mode,
"Sense": result["Sense"], "Sense": result["Sense"],
"Predicted LB": result["Predicted LB"],
"Predicted UB": result["Predicted UB"],
}, ignore_index=True) }, ignore_index=True)
groups = self.results.groupby("Instance") groups = self.results.groupby("Instance")
best_lower_bound = groups["Lower Bound"].transform("max") 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 pass
@abstractmethod @abstractmethod
def after_solve(self, solver, instance, model): def after_solve(self, solver, instance, model, results):
pass pass
@abstractmethod @abstractmethod

2
miplearn/components/lazy.py
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@@ -38,7 +38,7 @@ class LazyConstraintsComponent(Component):
cut = instance.build_lazy_constraint(model, v) cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut) solver.internal_solver.add_constraint(cut)
def after_solve(self, solver, instance, model): def after_solve(self, solver, instance, model, results):
pass pass
def fit(self, training_instances): def fit(self, training_instances):

9
miplearn/components/objective.py
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@@ -27,8 +27,13 @@ class ObjectiveValueComponent(Component):
instance.predicted_lb = lb instance.predicted_lb = lb
logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub)) logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
def after_solve(self, solver, instance, model): def after_solve(self, solver, instance, model, results):
pass 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): def fit(self, training_instances):
logger.debug("Extracting features...") logger.debug("Extracting features...")

2
miplearn/components/primal.py
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@@ -135,7 +135,7 @@ class PrimalSolutionComponent(Component):
else: else:
solver.internal_solver.set_warm_start(solution) solver.internal_solver.set_warm_start(solution)
def after_solve(self, solver, instance, model): def after_solve(self, solver, instance, model, results):
pass pass
def fit(self, training_instances): 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...") logger.debug("Calling after_solve callbacks...")
for component in self.components.values(): for component in self.components.values():
component.after_solve(self, instance, model) component.after_solve(self, instance, model, results)
# Store instance for future training # Store instance for future training
self.training_instances += [instance] self.training_instances += [instance]

4
miplearn/tests/test_benchmark.py
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@@ -27,11 +27,11 @@ def test_benchmark():
benchmark = BenchmarkRunner(test_solvers) benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances) benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2) 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") benchmark.save_results("/tmp/benchmark.csv")
assert os.path.isfile("/tmp/benchmark.csv") assert os.path.isfile("/tmp/benchmark.csv")
benchmark = BenchmarkRunner(test_solvers) benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results("/tmp/benchmark.csv") benchmark.load_results("/tmp/benchmark.csv")
assert benchmark.raw_results().values.shape == (12,13) assert benchmark.raw_results().values.shape == (12,16)