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Refactor StaticLazy

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This commit is contained in:
Alinson S. Xavier
2021-04-12 10:05:17 -05:00
parent e6672a45a0
commit cb62345acf
2 changed files with 91 additions and 169 deletions
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167
miplearn/components/static_lazy.py
View File

@@ -52,26 +52,35 @@ class StaticLazyConstraintsComponent(Component):
self.n_iterations: int = 0 self.n_iterations: int = 0
@overrides @overrides
def before_solve_mip_old( def after_solve_mip(
self, self,
solver: "LearningSolver", solver: "LearningSolver",
instance: "Instance", instance: "Instance",
model: Any, model: Any,
stats: LearningSolveStats, stats: LearningSolveStats,
features: Features, sample: Sample,
training_data: TrainingSample, ) -> None:
sample.after_mip.extra["lazy_enforced"] = self.enforced_cids
stats["LazyStatic: Restored"] = self.n_restored
stats["LazyStatic: Iterations"] = self.n_iterations
@overrides
def before_solve_mip(
self,
solver: "LearningSolver",
instance: "Instance",
model: Any,
stats: LearningSolveStats,
sample: Sample,
) -> None: ) -> None:
assert solver.internal_solver is not None assert solver.internal_solver is not None
assert features.instance is not None
assert features.constraints is not None
logger.info("Predicting violated (static) lazy constraints...") logger.info("Predicting violated (static) lazy constraints...")
if features.instance.lazy_constraint_count == 0: if sample.after_load.instance.lazy_constraint_count == 0:
logger.info("Instance does not have static lazy constraints. Skipping.") logger.info("Instance does not have static lazy constraints. Skipping.")
self.enforced_cids = set(self.sample_predict(instance, training_data)) self.enforced_cids = set(self.sample_predict(sample))
logger.info("Moving lazy constraints to the pool...") logger.info("Moving lazy constraints to the pool...")
self.pool = {} self.pool = {}
for (cid, cdict) in features.constraints.items(): for (cid, cdict) in sample.after_load.constraints.items():
if cdict.lazy and cid not in self.enforced_cids: if cdict.lazy and cid not in self.enforced_cids:
self.pool[cid] = cdict self.pool[cid] = cdict
solver.internal_solver.remove_constraint(cid) solver.internal_solver.remove_constraint(cid)
@@ -86,18 +95,17 @@ class StaticLazyConstraintsComponent(Component):
self.n_iterations = 0 self.n_iterations = 0
@overrides @overrides
def after_solve_mip_old( def fit_xy(
self, self,
solver: "LearningSolver", x: Dict[Hashable, np.ndarray],
instance: "Instance", y: Dict[Hashable, np.ndarray],
model: Any,
stats: LearningSolveStats,
features: Features,
training_data: TrainingSample,
) -> None: ) -> None:
training_data.lazy_enforced = self.enforced_cids for c in y.keys():
stats["LazyStatic: Restored"] = self.n_restored assert c in x
stats["LazyStatic: Iterations"] = self.n_iterations self.classifiers[c] = self.classifier_prototype.clone()
self.thresholds[c] = self.threshold_prototype.clone()
self.classifiers[c].fit(x[c], y[c])
self.thresholds[c].fit(self.classifiers[c], x[c], y[c])
@overrides @overrides
def iteration_cb( def iteration_cb(
@@ -120,6 +128,30 @@ class StaticLazyConstraintsComponent(Component):
) -> None: ) -> None:
self._check_and_add(solver) self._check_and_add(solver)
def sample_predict(self, sample: Sample) -> List[Hashable]:
x, y, cids = self._sample_xy_with_cids(sample)
enforced_cids: List[Hashable] = []
for category in x.keys():
if category not in self.classifiers:
continue
npx = np.array(x[category])
proba = self.classifiers[category].predict_proba(npx)
thr = self.thresholds[category].predict(npx)
pred = list(proba[:, 1] > thr[1])
for (i, is_selected) in enumerate(pred):
if is_selected:
enforced_cids += [cids[category][i]]
return enforced_cids
@overrides
def sample_xy(
self,
_: Optional[Instance],
sample: Sample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
x, y, _ = self._sample_xy_with_cids(sample)
return x, y
def _check_and_add(self, solver: "LearningSolver") -> bool: def _check_and_add(self, solver: "LearningSolver") -> bool:
assert solver.internal_solver is not None assert solver.internal_solver is not None
logger.info("Finding violated lazy constraints...") logger.info("Finding violated lazy constraints...")
@@ -145,69 +177,16 @@ class StaticLazyConstraintsComponent(Component):
else: else:
return False return False
def sample_predict( def _sample_xy_with_cids(
self, self, sample: Sample
instance: "Instance", ) -> Tuple[
sample: TrainingSample, Dict[Hashable, List[List[float]]],
) -> List[Hashable]: Dict[Hashable, List[List[float]]],
assert instance.features.constraints is not None Dict[Hashable, List[str]],
]:
x, y = self.sample_xy_old(instance, sample) x: Dict[Hashable, List[List[float]]] = {}
category_to_cids: Dict[Hashable, List[Hashable]] = {} y: Dict[Hashable, List[List[float]]] = {}
for (cid, cfeatures) in instance.features.constraints.items(): cids: Dict[Hashable, List[str]] = {}
if cfeatures.category is None:
continue
category = cfeatures.category
if category not in category_to_cids:
category_to_cids[category] = []
category_to_cids[category] += [cid]
enforced_cids: List[Hashable] = []
for category in x.keys():
if category not in self.classifiers:
continue
npx = np.array(x[category])
proba = self.classifiers[category].predict_proba(npx)
thr = self.thresholds[category].predict(npx)
pred = list(proba[:, 1] > thr[1])
for (i, is_selected) in enumerate(pred):
if is_selected:
enforced_cids += [category_to_cids[category][i]]
return enforced_cids
@overrides
def sample_xy_old(
self,
instance: "Instance",
sample: TrainingSample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
assert instance.features.constraints is not None
x: Dict = {}
y: Dict = {}
for (cid, cfeatures) in instance.features.constraints.items():
if not cfeatures.lazy:
continue
category = cfeatures.category
if category is None:
continue
if category not in x:
x[category] = []
y[category] = []
x[category] += [cfeatures.user_features]
if sample.lazy_enforced is not None:
if cid in sample.lazy_enforced:
y[category] += [[False, True]]
else:
y[category] += [[True, False]]
return x, y
@overrides
def sample_xy(
self,
_: Optional[Instance],
sample: Sample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
x: Dict = {}
y: Dict = {}
assert sample.after_load is not None assert sample.after_load is not None
assert sample.after_load.constraints is not None assert sample.after_load.constraints is not None
for (cid, constr) in sample.after_load.constraints.items(): for (cid, constr) in sample.after_load.constraints.items():
@@ -220,6 +199,7 @@ class StaticLazyConstraintsComponent(Component):
if category not in x: if category not in x:
x[category] = [] x[category] = []
y[category] = [] y[category] = []
cids[category] = []
# Features # Features
sf = sample.after_load sf = sample.after_load
@@ -231,25 +211,16 @@ class StaticLazyConstraintsComponent(Component):
assert sf.constraints[cid] is not None assert sf.constraints[cid] is not None
features.extend(sf.constraints[cid].to_list()) features.extend(sf.constraints[cid].to_list())
x[category].append(features) x[category].append(features)
cids[category].append(cid)
# Labels # Labels
if sample.after_mip is not None: if (
assert sample.after_mip.extra is not None (sample.after_mip is not None)
and (sample.after_mip.extra is not None)
and ("lazy_enforced" in sample.after_mip.extra)
):
if cid in sample.after_mip.extra["lazy_enforced"]: if cid in sample.after_mip.extra["lazy_enforced"]:
y[category] += [[False, True]] y[category] += [[False, True]]
else: else:
y[category] += [[True, False]] y[category] += [[True, False]]
return x, y return x, y, cids
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
) -> None:
for c in y.keys():
assert c in x
self.classifiers[c] = self.classifier_prototype.clone()
self.thresholds[c] = self.threshold_prototype.clone()
self.classifiers[c].fit(x[c], y[c])
self.thresholds[c].fit(self.classifiers[c], x[c], y[c])

93
tests/components/test_static_lazy.py
View File

@@ -12,7 +12,6 @@ from miplearn.classifiers import Classifier
from miplearn.classifiers.threshold import Threshold, MinProbabilityThreshold from miplearn.classifiers.threshold import Threshold, MinProbabilityThreshold
from miplearn.components.static_lazy import StaticLazyConstraintsComponent from miplearn.components.static_lazy import StaticLazyConstraintsComponent
from miplearn.features import ( from miplearn.features import (
TrainingSample,
InstanceFeatures, InstanceFeatures,
Features, Features,
Constraint, Constraint,
@@ -30,13 +29,16 @@ from miplearn.types import (
def sample() -> Sample: def sample() -> Sample:
sample = Sample( sample = Sample(
after_load=Features( after_load=Features(
instance=InstanceFeatures(
lazy_constraint_count=4,
),
constraints={ constraints={
"c1": Constraint(category="type-a", lazy=True), "c1": Constraint(category="type-a", lazy=True),
"c2": Constraint(category="type-a", lazy=True), "c2": Constraint(category="type-a", lazy=True),
"c3": Constraint(category="type-a", lazy=True), "c3": Constraint(category="type-a", lazy=True),
"c4": Constraint(category="type-b", lazy=True), "c4": Constraint(category="type-b", lazy=True),
"c5": Constraint(category="type-b", lazy=False), "c5": Constraint(category="type-b", lazy=False),
} },
), ),
after_lp=Features( after_lp=Features(
instance=InstanceFeatures(), instance=InstanceFeatures(),
@@ -71,61 +73,14 @@ def sample() -> Sample:
@pytest.fixture @pytest.fixture
def instance_old(features: Features) -> Instance: def instance(sample: Sample) -> Instance:
instance = Mock(spec=Instance) instance = Mock(spec=Instance)
instance.features = features instance.samples = [sample]
instance.has_static_lazy_constraints = Mock(return_value=True) instance.has_static_lazy_constraints = Mock(return_value=True)
return instance return instance
@pytest.fixture def test_usage_with_solver(instance: Instance) -> None:
def sample_old() -> TrainingSample:
return TrainingSample(
lazy_enforced={"c1", "c2", "c4"},
)
@pytest.fixture
def features() -> Features:
return Features(
instance=InstanceFeatures(
user_features=[0],
lazy_constraint_count=4,
),
constraints={
"c1": Constraint(
category="type-a",
user_features=[1.0, 1.0],
lazy=True,
),
"c2": Constraint(
category="type-a",
user_features=[1.0, 2.0],
lazy=True,
),
"c3": Constraint(
category="type-a",
user_features=[1.0, 3.0],
lazy=True,
),
"c4": Constraint(
category="type-b",
user_features=[1.0, 4.0, 0.0],
lazy=True,
),
"c5": Constraint(
category="type-b",
user_features=[1.0, 5.0, 0.0],
lazy=False,
),
},
)
def test_usage_with_solver(instance_old: Instance) -> None:
assert instance_old.features is not None
assert instance_old.features.constraints is not None
solver = Mock(spec=LearningSolver) solver = Mock(spec=LearningSolver)
solver.use_lazy_cb = False solver.use_lazy_cb = False
solver.gap_tolerance = 1e-4 solver.gap_tolerance = 1e-4
@@ -157,17 +112,17 @@ def test_usage_with_solver(instance_old: Instance) -> None:
) )
) )
sample_old: TrainingSample = TrainingSample()
stats: LearningSolveStats = {} stats: LearningSolveStats = {}
sample = instance.samples[0]
del sample.after_mip.extra["lazy_enforced"]
# LearningSolver calls before_solve_mip # LearningSolver calls before_solve_mip
component.before_solve_mip_old( component.before_solve_mip(
solver=solver, solver=solver,
instance=instance_old, instance=instance,
model=None, model=None,
stats=stats, stats=stats,
features=instance_old.features, sample=sample,
training_data=sample_old,
) )
# Should ask ML to predict whether each lazy constraint should be enforced # Should ask ML to predict whether each lazy constraint should be enforced
@@ -179,19 +134,19 @@ def test_usage_with_solver(instance_old: Instance) -> None:
internal.remove_constraint.assert_has_calls([call("c3")]) internal.remove_constraint.assert_has_calls([call("c3")])
# LearningSolver calls after_iteration (first time) # LearningSolver calls after_iteration (first time)
should_repeat = component.iteration_cb(solver, instance_old, None) should_repeat = component.iteration_cb(solver, instance, None)
assert should_repeat assert should_repeat
# Should ask internal solver to verify if constraints in the pool are # Should ask internal solver to verify if constraints in the pool are
# satisfied and add the ones that are not # satisfied and add the ones that are not
c3 = instance_old.features.constraints["c3"] c3 = sample.after_load.constraints["c3"]
internal.is_constraint_satisfied.assert_called_once_with(c3, tol=1.0) internal.is_constraint_satisfied.assert_called_once_with(c3, tol=1.0)
internal.is_constraint_satisfied.reset_mock() internal.is_constraint_satisfied.reset_mock()
internal.add_constraint.assert_called_once_with(c3, name="c3") internal.add_constraint.assert_called_once_with(c3, name="c3")
internal.add_constraint.reset_mock() internal.add_constraint.reset_mock()
# LearningSolver calls after_iteration (second time) # LearningSolver calls after_iteration (second time)
should_repeat = component.iteration_cb(solver, instance_old, None) should_repeat = component.iteration_cb(solver, instance, None)
assert not should_repeat assert not should_repeat
# The lazy constraint pool should be empty by now, so no calls should be made # The lazy constraint pool should be empty by now, so no calls should be made
@@ -199,18 +154,17 @@ def test_usage_with_solver(instance_old: Instance) -> None:
internal.add_constraint.assert_not_called() internal.add_constraint.assert_not_called()
# LearningSolver calls after_solve_mip # LearningSolver calls after_solve_mip
component.after_solve_mip_old( component.after_solve_mip(
solver=solver, solver=solver,
instance=instance_old, instance=instance,
model=None, model=None,
stats=stats, stats=stats,
features=instance_old.features, sample=sample,
training_data=sample_old,
) )
# Should update training sample # Should update training sample
assert sample_old.lazy_enforced == {"c1", "c2", "c3", "c4"} assert sample.after_mip.extra["lazy_enforced"] == {"c1", "c2", "c3", "c4"}
#
# Should update stats # Should update stats
assert stats["LazyStatic: Removed"] == 1 assert stats["LazyStatic: Removed"] == 1
assert stats["LazyStatic: Kept"] == 3 assert stats["LazyStatic: Kept"] == 3
@@ -218,10 +172,7 @@ def test_usage_with_solver(instance_old: Instance) -> None:
assert stats["LazyStatic: Iterations"] == 1 assert stats["LazyStatic: Iterations"] == 1
def test_sample_predict( def test_sample_predict(sample: Sample) -> None:
instance_old: Instance,
sample_old: TrainingSample,
) -> None:
comp = StaticLazyConstraintsComponent() comp = StaticLazyConstraintsComponent()
comp.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5]) comp.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
comp.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5]) comp.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5])
@@ -239,7 +190,7 @@ def test_sample_predict(
[0.0, 1.0], # c4 [0.0, 1.0], # c4
] ]
) )
pred = comp.sample_predict(instance_old, sample_old) pred = comp.sample_predict(sample)
assert pred == ["c1", "c2", "c4"] assert pred == ["c1", "c2", "c4"]