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Update DynamicLazyConstraintsComponent

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This commit is contained in:
Alinson S. Xavier
2021-04-13 08:42:06 -05:00
parent b5411b8950
commit a4433916e5
8 changed files with 144 additions and 63 deletions
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2
miplearn/components/component.py
View File

@@ -196,7 +196,7 @@ class Component(EnforceOverrides):
) -> None:
x, y = self.xy_instances(training_instances)
for cat in x.keys():
x[cat] = np.array(x[cat])
x[cat] = np.array(x[cat], dtype=np.float32)
y[cat] = np.array(y[cat])
self.fit_xy(x, y)

61
miplearn/components/dynamic_common.py
View File

@@ -105,7 +105,10 @@ class DynamicConstraintsComponent(Component):
features.extend(sample.after_lp.instance.to_list())
features.extend(instance.get_constraint_features(cid))
for ci in features:
assert isinstance(ci, float)
assert isinstance(ci, float), (
f"Constraint features must be a list of floats. "
f"Found {ci.__class__.__name__} instead."
)
x[category].append(features)
cids[category].append(cid)
@@ -137,7 +140,7 @@ class DynamicConstraintsComponent(Component):
x, y, _ = self.sample_xy_with_cids(instance, sample)
return x, y
def sample_predict(
def sample_predict_old(
self,
instance: Instance,
sample: TrainingSample,
@@ -160,6 +163,29 @@ class DynamicConstraintsComponent(Component):
pred += [cids[category][i]]
return pred
def sample_predict(
self,
instance: Instance,
sample: Sample,
) -> List[Hashable]:
pred: List[Hashable] = []
if len(self.known_cids) == 0:
logger.info("Classifiers not fitted. Skipping.")
return pred
x, _, cids = self.sample_xy_with_cids(instance, sample)
for category in x.keys():
assert category in self.classifiers
assert category in self.thresholds
clf = self.classifiers[category]
thr = self.thresholds[category]
nx = np.array(x[category])
proba = clf.predict_proba(nx)
t = thr.predict(nx)
for i in range(proba.shape[0]):
if proba[i][1] > t[1]:
pred += [cids[category][i]]
return pred
@overrides
def fit_old(self, training_instances: List[Instance]) -> None:
collected_cids = set()
@@ -174,6 +200,24 @@ class DynamicConstraintsComponent(Component):
self.known_cids.extend(sorted(collected_cids))
super().fit_old(training_instances)
@overrides
def fit(self, training_instances: List[Instance]) -> None:
collected_cids = set()
for instance in training_instances:
instance.load()
for sample in instance.samples:
if (
sample.after_mip is None
or sample.after_mip.extra is None
or sample.after_mip.extra[self.attr] is None
):
continue
collected_cids |= sample.after_mip.extra[self.attr]
instance.free()
self.known_cids.clear()
self.known_cids.extend(sorted(collected_cids))
super().fit(training_instances)
@overrides
def fit_xy(
self,
@@ -189,12 +233,15 @@ class DynamicConstraintsComponent(Component):
self.thresholds[category].fit(self.classifiers[category], npx, npy)
@overrides
def sample_evaluate_old(
def sample_evaluate(
self,
instance: Instance,
sample: TrainingSample,
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
assert getattr(sample, self.attr) is not None
assert sample.after_mip is not None
assert sample.after_mip.extra is not None
assert self.attr in sample.after_mip.extra
actual = sample.after_mip.extra[self.attr]
pred = set(self.sample_predict(instance, sample))
tp: Dict[Hashable, int] = {}
tn: Dict[Hashable, int] = {}
@@ -210,12 +257,12 @@ class DynamicConstraintsComponent(Component):
fp[category] = 0
fn[category] = 0
if cid in pred:
if cid in getattr(sample, self.attr):
if cid in actual:
tp[category] += 1
else:
fp[category] += 1
else:
if cid in getattr(sample, self.attr):
if cid in actual:
fn[category] += 1
else:
tn[category] += 1

49
miplearn/components/dynamic_lazy.py
View File

@@ -3,7 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import Dict, List, TYPE_CHECKING, Hashable, Tuple, Any, Optional
from typing import Dict, List, TYPE_CHECKING, Hashable, Tuple, Any, Optional, Set
import numpy as np
from overrides import overrides
@@ -41,6 +41,7 @@ class DynamicLazyConstraintsComponent(Component):
self.classifiers = self.dynamic.classifiers
self.thresholds = self.dynamic.thresholds
self.known_cids = self.dynamic.known_cids
self.lazy_enforced: Set[str] = set()
@staticmethod
def enforce(
@@ -54,21 +55,33 @@ class DynamicLazyConstraintsComponent(Component):
instance.enforce_lazy_constraint(solver.internal_solver, model, cid)
@overrides
def before_solve_mip_old(
def before_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,
model: Any,
stats: LearningSolveStats,
features: Features,
training_data: TrainingSample,
sample: Sample,
) -> None:
training_data.lazy_enforced = set()
self.lazy_enforced.clear()
logger.info("Predicting violated (dynamic) lazy constraints...")
cids = self.dynamic.sample_predict(instance, training_data)
cids = self.dynamic.sample_predict(instance, sample)
logger.info("Enforcing %d lazy constraints..." % len(cids))
self.enforce(cids, instance, model, solver)
@overrides
def after_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,
model: Any,
stats: LearningSolveStats,
sample: Sample,
) -> None:
assert sample.after_mip is not None
assert sample.after_mip.extra is not None
sample.after_mip.extra["lazy_enforced"] = set(self.lazy_enforced)
@overrides
def iteration_cb(
self,
@@ -83,23 +96,13 @@ class DynamicLazyConstraintsComponent(Component):
logger.debug("No violations found")
return False
else:
sample = instance.training_data[-1]
assert sample.lazy_enforced is not None
sample.lazy_enforced |= set(cids)
self.lazy_enforced |= set(cids)
logger.debug(" %d violations found" % len(cids))
self.enforce(cids, instance, model, solver)
return True
# Delegate ML methods to self.dynamic
# -------------------------------------------------------------------
@overrides
def sample_xy_old(
self,
instance: Instance,
sample: TrainingSample,
) -> Tuple[Dict, Dict]:
return self.dynamic.sample_xy_old(instance, sample)
@overrides
def sample_xy(
self,
@@ -111,13 +114,13 @@ class DynamicLazyConstraintsComponent(Component):
def sample_predict(
self,
instance: Instance,
sample: TrainingSample,
sample: Sample,
) -> List[Hashable]:
return self.dynamic.sample_predict(instance, sample)
@overrides
def fit_old(self, training_instances: List[Instance]) -> None:
self.dynamic.fit_old(training_instances)
def fit(self, training_instances: List[Instance]) -> None:
self.dynamic.fit(training_instances)
@overrides
def fit_xy(
@@ -128,9 +131,9 @@ class DynamicLazyConstraintsComponent(Component):
self.dynamic.fit_xy(x, y)
@overrides
def sample_evaluate_old(
def sample_evaluate(
self,
instance: Instance,
sample: TrainingSample,
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
return self.dynamic.sample_evaluate_old(instance, sample)
return self.dynamic.sample_evaluate(instance, sample)

2
miplearn/components/dynamic_user_cuts.py
View File

@@ -51,7 +51,7 @@ class UserCutsComponent(Component):
self.enforced.clear()
self.n_added_in_callback = 0
logger.info("Predicting violated user cuts...")
cids = self.dynamic.sample_predict(instance, training_data)
cids = self.dynamic.sample_predict_old(instance, training_data)
logger.info("Enforcing %d user cuts ahead-of-time..." % len(cids))
for cid in cids:
instance.enforce_user_cut(solver.internal_solver, model, cid)

8
miplearn/instance/base.py
View File

@@ -62,9 +62,9 @@ class Instance(ABC, EnforceOverrides):
the problem. If two instances map into arrays of different lengths,
they cannot be solved by the same LearningSolver object.
By default, returns [0].
By default, returns [0.0].
"""
return [0]
return [0.0]
def get_variable_features(self, var_name: VariableName) -> List[float]:
"""
@@ -81,9 +81,9 @@ class Instance(ABC, EnforceOverrides):
length for all variables within the same category, for all relevant instances
of the problem.
By default, returns [0].
By default, returns [0.0].
"""
return [0]
return [0.0]
def get_variable_category(self, var_name: VariableName) -> Optional[Category]:
"""

3
miplearn/solvers/learning.py
View File

@@ -159,6 +159,7 @@ class LearningSolver:
# -------------------------------------------------------
logger.info("Extracting features (after-load)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
features.extra = {}
instance.features.__dict__ = features.__dict__
sample.after_load = features
@@ -204,6 +205,7 @@ class LearningSolver:
# -------------------------------------------------------
logger.info("Extracting features (after-lp)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
features.extra = {}
features.lp_solve = lp_stats
sample.after_lp = features
@@ -267,6 +269,7 @@ class LearningSolver:
logger.info("Extracting features (after-mip)...")
features = FeaturesExtractor(self.internal_solver).extract(instance)
features.mip_solve = mip_stats
features.extra = {}
sample.after_mip = features
# Add some information to training_sample

77
tests/components/test_dynamic_lazy.py
View File

@@ -83,15 +83,20 @@ def training_instances() -> List[Instance]:
instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
instances[0].samples = [
Sample(
after_lp=Features(
instance=InstanceFeatures(),
),
after_lp=Features(instance=InstanceFeatures()),
after_mip=Features(extra={"lazy_enforced": {"c1", "c2"}}),
)
),
Sample(
after_lp=Features(instance=InstanceFeatures()),
after_mip=Features(extra={"lazy_enforced": {"c2", "c3"}}),
),
]
instances[0].samples[0].after_lp.instance.to_list = Mock( # type: ignore
return_value=[5.0]
)
instances[0].samples[1].after_lp.instance.to_list = Mock( # type: ignore
return_value=[5.0]
)
instances[0].get_constraint_category = Mock( # type: ignore
side_effect=lambda cid: {
"c1": "type-a",
@@ -108,7 +113,30 @@ def training_instances() -> List[Instance]:
"c4": [3.0, 4.0],
}[cid]
)
instances[1].samples = [
Sample(
after_lp=Features(instance=InstanceFeatures()),
after_mip=Features(extra={"lazy_enforced": {"c3", "c4"}}),
)
]
instances[1].samples[0].after_lp.instance.to_list = Mock( # type: ignore
return_value=[8.0]
)
instances[1].get_constraint_category = Mock( # type: ignore
side_effect=lambda cid: {
"c1": None,
"c2": "type-a",
"c3": "type-b",
"c4": "type-b",
}[cid]
)
instances[1].get_constraint_features = Mock( # type: ignore
side_effect=lambda cid: {
"c2": [7.0, 8.0, 9.0],
"c3": [5.0, 6.0],
"c4": [7.0, 8.0],
}[cid]
)
return instances
@@ -131,11 +159,11 @@ def test_sample_xy(training_instances: List[Instance]) -> None:
assert_equals(y_actual, y_expected)
def test_fit_old(training_instances_old: List[Instance]) -> None:
def test_fit(training_instances: List[Instance]) -> None:
clf = Mock(spec=Classifier)
clf.clone = Mock(side_effect=lambda: Mock(spec=Classifier))
comp = DynamicLazyConstraintsComponent(classifier=clf)
comp.fit_old(training_instances_old)
comp.fit(training_instances)
assert clf.clone.call_count == 2
assert "type-a" in comp.classifiers
@@ -145,11 +173,11 @@ def test_fit_old(training_instances_old: List[Instance]) -> None:
clf_a.fit.call_args[0][0], # type: ignore
np.array(
[
[50.0, 1.0, 2.0, 3.0],
[50.0, 4.0, 5.0, 6.0],
[50.0, 1.0, 2.0, 3.0],
[50.0, 4.0, 5.0, 6.0],
[80.0, 7.0, 8.0, 9.0],
[5.0, 1.0, 2.0, 3.0],
[5.0, 4.0, 5.0, 6.0],
[5.0, 1.0, 2.0, 3.0],
[5.0, 4.0, 5.0, 6.0],
[8.0, 7.0, 8.0, 9.0],
]
),
)
@@ -173,12 +201,12 @@ def test_fit_old(training_instances_old: List[Instance]) -> None:
clf_b.fit.call_args[0][0], # type: ignore
np.array(
[
[50.0, 1.0, 2.0],
[50.0, 3.0, 4.0],
[50.0, 1.0, 2.0],
[50.0, 3.0, 4.0],
[80.0, 5.0, 6.0],
[80.0, 7.0, 8.0],
[5.0, 1.0, 2.0],
[5.0, 3.0, 4.0],
[5.0, 1.0, 2.0],
[5.0, 3.0, 4.0],
[8.0, 5.0, 6.0],
[8.0, 7.0, 8.0],
]
),
)
@@ -197,7 +225,7 @@ def test_fit_old(training_instances_old: List[Instance]) -> None:
)
def test_sample_predict_evaluate_old(training_instances_old: List[Instance]) -> None:
def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
comp = DynamicLazyConstraintsComponent()
comp.known_cids.extend(["c1", "c2", "c3", "c4"])
comp.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
@@ -211,15 +239,14 @@ def test_sample_predict_evaluate_old(training_instances_old: List[Instance]) ->
side_effect=lambda _: np.array([[0.9, 0.1], [0.1, 0.9]])
)
pred = comp.sample_predict(
training_instances_old[0],
training_instances_old[0].training_data[0],
training_instances[0],
training_instances[0].samples[0],
)
assert pred == ["c1", "c4"]
ev = comp.sample_evaluate_old(
training_instances_old[0],
training_instances_old[0].training_data[0],
ev = comp.sample_evaluate(
training_instances[0],
training_instances[0].samples[0],
)
print(ev)
assert ev == {
"type-a": classifier_evaluation_dict(tp=1, fp=0, tn=0, fn=1),
"type-b": classifier_evaluation_dict(tp=0, fp=1, tn=1, fn=0),

5
tests/problems/test_tsp.py
View File

@@ -67,8 +67,9 @@ def test_subtour() -> None:
instance = TravelingSalesmanInstance(n_cities, distances)
solver = LearningSolver()
solver.solve(instance)
assert instance.training_data[0].lazy_enforced is not None
assert len(instance.training_data[0].lazy_enforced) > 0
lazy_enforced = instance.samples[0].after_mip.extra["lazy_enforced"]
assert lazy_enforced is not None
assert len(lazy_enforced) > 0
solution = instance.training_data[0].solution
assert solution is not None
assert solution["x[(0, 1)]"] == 1.0