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Use np.ndarray for constraint methods in Instance

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Alinson S. Xavier
2021-08-09 20:11:37 -05:00
parent 895cb962b6
commit e852d5cdca
16 changed files with 532 additions and 429 deletions
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122
miplearn/components/dynamic_common.py
View File

@@ -8,12 +8,14 @@ from typing import Dict, List, Tuple, Optional, Any, Set
import numpy as np
from overrides import overrides
from miplearn.features.extractor import FeaturesExtractor
from miplearn.classifiers import Classifier
from miplearn.classifiers.threshold import Threshold
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.features.sample import Sample
from miplearn.instance.base import Instance
from miplearn.types import ConstraintCategory, ConstraintName
logger = logging.getLogger(__name__)
@@ -32,9 +34,9 @@ class DynamicConstraintsComponent(Component):
assert isinstance(classifier, Classifier)
self.threshold_prototype: Threshold = threshold
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[str, Classifier] = {}
self.thresholds: Dict[str, Threshold] = {}
self.known_cids: List[str] = []
self.classifiers: Dict[ConstraintCategory, Classifier] = {}
self.thresholds: Dict[ConstraintCategory, Threshold] = {}
self.known_cids: List[ConstraintName] = []
self.attr = attr
def sample_xy_with_cids(
@@ -42,51 +44,45 @@ class DynamicConstraintsComponent(Component):
instance: Optional[Instance],
sample: Sample,
) -> Tuple[
Dict[str, List[List[float]]],
Dict[str, List[List[bool]]],
Dict[str, List[str]],
Dict[ConstraintCategory, List[List[float]]],
Dict[ConstraintCategory, List[List[bool]]],
Dict[ConstraintCategory, List[ConstraintName]],
]:
if len(self.known_cids) == 0:
return {}, {}, {}
assert instance is not None
x: Dict[str, List[List[float]]] = {}
y: Dict[str, List[List[bool]]] = {}
cids: Dict[str, List[str]] = {}
constr_categories_dict = instance.get_constraint_categories()
constr_features_dict = instance.get_constraint_features()
x: Dict[ConstraintCategory, List[List[float]]] = {}
y: Dict[ConstraintCategory, List[List[bool]]] = {}
cids: Dict[ConstraintCategory, List[ConstraintName]] = {}
known_cids = np.array(self.known_cids, dtype="S")
# Get user-provided constraint features
(
constr_features,
constr_categories,
constr_lazy,
) = FeaturesExtractor._extract_user_features_constrs(instance, known_cids)
# Augment with instance features
instance_features = sample.get_array("static_instance_features")
assert instance_features is not None
for cid in self.known_cids:
# Initialize categories
if cid in constr_categories_dict:
category = constr_categories_dict[cid]
else:
category = cid
if category is None:
continue
if category not in x:
x[category] = []
y[category] = []
cids[category] = []
constr_features = np.hstack(
[
instance_features.reshape(1, -1).repeat(len(known_cids), axis=0),
constr_features,
]
)
assert len(known_cids) == constr_features.shape[0]
# Features
features: List[float] = []
features.extend(instance_features)
if cid in constr_features_dict:
features.extend(constr_features_dict[cid])
for ci in features:
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)
# Labels
enforced_cids = sample.get_set(self.attr)
categories = np.unique(constr_categories)
for c in categories:
x[c] = constr_features[constr_categories == c].tolist()
cids[c] = known_cids[constr_categories == c].tolist()
enforced_cids = np.array(list(sample.get_set(self.attr)), dtype="S")
if enforced_cids is not None:
if cid in enforced_cids:
y[category] += [[False, True]]
else:
y[category] += [[True, False]]
tmp = np.isin(cids[c], enforced_cids).reshape(-1, 1)
y[c] = np.hstack([~tmp, tmp]).tolist() # type: ignore
return x, y, cids
@overrides
@@ -111,8 +107,8 @@ class DynamicConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> List[str]:
pred: List[str] = []
) -> List[ConstraintName]:
pred: List[ConstraintName] = []
if len(self.known_cids) == 0:
logger.info("Classifiers not fitted. Skipping.")
return pred
@@ -137,8 +133,8 @@ class DynamicConstraintsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
x: Dict[ConstraintCategory, np.ndarray],
y: Dict[ConstraintCategory, np.ndarray],
) -> None:
for category in x.keys():
self.classifiers[category] = self.classifier_prototype.clone()
@@ -153,40 +149,20 @@ class DynamicConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> Dict[str, Dict[str, float]]:
) -> Dict[str, float]:
actual = sample.get_set(self.attr)
assert actual is not None
pred = set(self.sample_predict(instance, sample))
tp: Dict[str, int] = {}
tn: Dict[str, int] = {}
fp: Dict[str, int] = {}
fn: Dict[str, int] = {}
constr_categories_dict = instance.get_constraint_categories()
tp, tn, fp, fn = 0, 0, 0, 0
for cid in self.known_cids:
if cid not in constr_categories_dict:
continue
category = constr_categories_dict[cid]
if category not in tp.keys():
tp[category] = 0
tn[category] = 0
fp[category] = 0
fn[category] = 0
if cid in pred:
if cid in actual:
tp[category] += 1
tp += 1
else:
fp[category] += 1
fp += 1
else:
if cid in actual:
fn[category] += 1
fn += 1
else:
tn[category] += 1
return {
category: classifier_evaluation_dict(
tp=tp[category],
tn=tn[category],
fp=fp[category],
fn=fn[category],
)
for category in tp.keys()
}
tn += 1
return classifier_evaluation_dict(tp=tp, tn=tn, fp=fp, fn=fn)

14
miplearn/components/dynamic_lazy.py
View File

@@ -15,7 +15,7 @@ from miplearn.components.component import Component
from miplearn.components.dynamic_common import DynamicConstraintsComponent
from miplearn.features.sample import Sample
from miplearn.instance.base import Instance
from miplearn.types import LearningSolveStats
from miplearn.types import LearningSolveStats, ConstraintName, ConstraintCategory
logger = logging.getLogger(__name__)
@@ -41,11 +41,11 @@ 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()
self.lazy_enforced: Set[ConstraintName] = set()
@staticmethod
def enforce(
cids: List[str],
cids: List[ConstraintName],
instance: Instance,
model: Any,
solver: "LearningSolver",
@@ -117,7 +117,7 @@ class DynamicLazyConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> List[str]:
) -> List[ConstraintName]:
return self.dynamic.sample_predict(instance, sample)
@overrides
@@ -127,8 +127,8 @@ class DynamicLazyConstraintsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
x: Dict[ConstraintCategory, np.ndarray],
y: Dict[ConstraintCategory, np.ndarray],
) -> None:
self.dynamic.fit_xy(x, y)
@@ -137,5 +137,5 @@ class DynamicLazyConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> Dict[str, Dict[str, float]]:
) -> Dict[ConstraintCategory, Dict[str, float]]:
return self.dynamic.sample_evaluate(instance, sample)

14
miplearn/components/dynamic_user_cuts.py
View File

@@ -15,7 +15,7 @@ from miplearn.components.component import Component
from miplearn.components.dynamic_common import DynamicConstraintsComponent
from miplearn.features.sample import Sample
from miplearn.instance.base import Instance
from miplearn.types import LearningSolveStats
from miplearn.types import LearningSolveStats, ConstraintName, ConstraintCategory
logger = logging.getLogger(__name__)
@@ -34,7 +34,7 @@ class UserCutsComponent(Component):
threshold=threshold,
attr="mip_user_cuts_enforced",
)
self.enforced: Set[str] = set()
self.enforced: Set[ConstraintName] = set()
self.n_added_in_callback = 0
@overrides
@@ -71,7 +71,7 @@ class UserCutsComponent(Component):
for cid in cids:
if cid in self.enforced:
continue
assert isinstance(cid, str)
assert isinstance(cid, ConstraintName)
instance.enforce_user_cut(solver.internal_solver, model, cid)
self.enforced.add(cid)
self.n_added_in_callback += 1
@@ -110,7 +110,7 @@ class UserCutsComponent(Component):
self,
instance: "Instance",
sample: Sample,
) -> List[str]:
) -> List[ConstraintName]:
return self.dynamic.sample_predict(instance, sample)
@overrides
@@ -120,8 +120,8 @@ class UserCutsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
x: Dict[ConstraintCategory, np.ndarray],
y: Dict[ConstraintCategory, np.ndarray],
) -> None:
self.dynamic.fit_xy(x, y)
@@ -130,5 +130,5 @@ class UserCutsComponent(Component):
self,
instance: "Instance",
sample: Sample,
) -> Dict[str, Dict[str, float]]:
) -> Dict[ConstraintCategory, Dict[str, float]]:
return self.dynamic.sample_evaluate(instance, sample)

35
miplearn/components/static_lazy.py
View File

@@ -15,7 +15,7 @@ from miplearn.components.component import Component
from miplearn.features.sample import Sample
from miplearn.solvers.internal import Constraints
from miplearn.instance.base import Instance
from miplearn.types import LearningSolveStats
from miplearn.types import LearningSolveStats, ConstraintName, ConstraintCategory
logger = logging.getLogger(__name__)
@@ -24,7 +24,7 @@ if TYPE_CHECKING:
class LazyConstraint:
def __init__(self, cid: str, obj: Any) -> None:
def __init__(self, cid: ConstraintName, obj: Any) -> None:
self.cid = cid
self.obj = obj
@@ -44,11 +44,11 @@ class StaticLazyConstraintsComponent(Component):
assert isinstance(classifier, Classifier)
self.classifier_prototype: Classifier = classifier
self.threshold_prototype: Threshold = threshold
self.classifiers: Dict[str, Classifier] = {}
self.thresholds: Dict[str, Threshold] = {}
self.classifiers: Dict[ConstraintCategory, Classifier] = {}
self.thresholds: Dict[ConstraintCategory, Threshold] = {}
self.pool: Constraints = Constraints()
self.violation_tolerance: float = violation_tolerance
self.enforced_cids: Set[str] = set()
self.enforced_cids: Set[ConstraintName] = set()
self.n_restored: int = 0
self.n_iterations: int = 0
@@ -105,8 +105,8 @@ class StaticLazyConstraintsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
x: Dict[ConstraintCategory, np.ndarray],
y: Dict[ConstraintCategory, np.ndarray],
) -> None:
for c in y.keys():
assert c in x
@@ -136,9 +136,9 @@ class StaticLazyConstraintsComponent(Component):
) -> None:
self._check_and_add(solver)
def sample_predict(self, sample: Sample) -> List[str]:
def sample_predict(self, sample: Sample) -> List[ConstraintName]:
x, y, cids = self._sample_xy_with_cids(sample)
enforced_cids: List[str] = []
enforced_cids: List[ConstraintName] = []
for category in x.keys():
if category not in self.classifiers:
continue
@@ -156,7 +156,10 @@ class StaticLazyConstraintsComponent(Component):
self,
_: Optional[Instance],
sample: Sample,
) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
) -> Tuple[
Dict[ConstraintCategory, List[List[float]]],
Dict[ConstraintCategory, List[List[float]]],
]:
x, y, __ = self._sample_xy_with_cids(sample)
return x, y
@@ -197,13 +200,13 @@ class StaticLazyConstraintsComponent(Component):
def _sample_xy_with_cids(
self, sample: Sample
) -> Tuple[
Dict[str, List[List[float]]],
Dict[str, List[List[float]]],
Dict[str, List[str]],
Dict[ConstraintCategory, List[List[float]]],
Dict[ConstraintCategory, List[List[float]]],
Dict[ConstraintCategory, List[ConstraintName]],
]:
x: Dict[str, List[List[float]]] = {}
y: Dict[str, List[List[float]]] = {}
cids: Dict[str, List[str]] = {}
x: Dict[ConstraintCategory, List[List[float]]] = {}
y: Dict[ConstraintCategory, List[List[float]]] = {}
cids: Dict[ConstraintCategory, List[ConstraintName]] = {}
instance_features = sample.get_vector("static_instance_features")
constr_features = sample.get_vector_list("lp_constr_features")
constr_names = sample.get_array("static_constr_names")