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Make xy_sample receive features, not instances

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This commit is contained in:
Alinson S. Xavier
2021-03-31 09:57:57 -05:00
parent 8fc9979b37
commit fe7bad885c
12 changed files with 158 additions and 119 deletions
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24
miplearn/components/component.py
View File

@@ -3,11 +3,11 @@
# Released under the modified BSD license. See COPYING.md for more details.
import numpy as np
from typing import Any, List, Union, TYPE_CHECKING, Tuple, Dict
from typing import Any, List, Union, TYPE_CHECKING, Tuple, Dict, Optional
from miplearn.extractors import InstanceIterator
from miplearn.instance import Instance
from miplearn.types import LearningSolveStats, TrainingSample
from miplearn.types import LearningSolveStats, TrainingSample, Features
if TYPE_CHECKING:
from miplearn.solvers.learning import LearningSolver
@@ -133,14 +133,16 @@ class Component:
@staticmethod
def xy_sample(
instance: Any,
training_sample: TrainingSample,
) -> Tuple[Dict, Dict]:
features: Features,
sample: TrainingSample,
) -> Optional[Tuple[Dict, Dict]]:
"""
Given a training sample, returns a pair of x and y dictionaries containing,
respectively, the matrices of ML features and the labels for the sample.
Given a set of features and a training sample, returns a pair of x and y
dictionaries containing, respectively, the matrices of ML features and the
labels for the sample. If the training sample does not include label
information, returns None.
"""
return {}, {}
return None
def xy_instances(
self,
@@ -149,8 +151,12 @@ class Component:
x_combined: Dict = {}
y_combined: Dict = {}
for instance in InstanceIterator(instances):
assert isinstance(instance, Instance)
for sample in instance.training_data:
x_sample, y_sample = self.xy_sample(instance, sample)
xy = self.xy_sample(instance.features, sample)
if xy is None:
continue
x_sample, y_sample = xy
for cat in x_sample.keys():
if cat not in x_combined:
x_combined[cat] = []

17
miplearn/components/lazy_static.py
View File

@@ -5,14 +5,14 @@
import logging
import sys
from copy import deepcopy
from typing import Any, Dict, Tuple
from typing import Any, Dict, Tuple, Optional
import numpy as np
from tqdm.auto import tqdm
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components.component import Component
from miplearn.types import TrainingSample
from miplearn.types import TrainingSample, Features
logger = logging.getLogger(__name__)
@@ -207,15 +207,16 @@ class StaticLazyConstraintsComponent(Component):
@staticmethod
def xy_sample(
instance: Any,
features: Features,
sample: TrainingSample,
) -> Tuple[Dict, Dict]:
) -> Optional[Tuple[Dict, Dict]]:
if "LazyStatic: Enforced" not in sample:
return None
x: Dict = {}
y: Dict = {}
if "LazyStatic: All" not in sample:
return x, y
for cid in sorted(sample["LazyStatic: All"]):
cfeatures = instance.features["Constraints"][cid]
for (cid, cfeatures) in features["Constraints"].items():
if not cfeatures["Lazy"]:
continue
category = cfeatures["Category"]
if category is None:
continue

26
miplearn/components/objective.py
View File

@@ -19,7 +19,7 @@ from miplearn.classifiers import Regressor
from miplearn.components.component import Component
from miplearn.extractors import InstanceIterator
from miplearn.instance import Instance
from miplearn.types import MIPSolveStats, TrainingSample, LearningSolveStats
from miplearn.types import MIPSolveStats, TrainingSample, LearningSolveStats, Features
if TYPE_CHECKING:
from miplearn.solvers.learning import LearningSolver
@@ -164,18 +164,20 @@ class ObjectiveValueComponent(Component):
@staticmethod
def xy_sample(
instance: Any,
features: Features,
sample: TrainingSample,
) -> Tuple[Dict, Dict]:
x: Dict = {}
y: Dict = {}
) -> Optional[Tuple[Dict, Dict]]:
if "Lower bound" not in sample:
return x, y
features = instance.features["Instance"]["User features"]
return None
f = features["Instance"]["User features"]
if "LP value" in sample and sample["LP value"] is not None:
features += [sample["LP value"]]
x["Lower bound"] = [features]
x["Upper bound"] = [features]
y["Lower bound"] = [[sample["Lower bound"]]]
y["Upper bound"] = [[sample["Upper bound"]]]
f += [sample["LP value"]]
x = {
"Lower bound": [f],
"Upper bound": [f],
}
y = {
"Lower bound": [[sample["Lower bound"]]],
"Upper bound": [[sample["Upper bound"]]],
}
return x, y

13
miplearn/components/primal.py
View File

@@ -211,15 +211,15 @@ class PrimalSolutionComponent(Component):
@staticmethod
def xy_sample(
instance: Any,
features: Features,
sample: TrainingSample,
) -> Tuple[Dict, Dict]:
) -> Optional[Tuple[Dict, Dict]]:
if "Solution" not in sample:
return {}, {}
return None
assert sample["Solution"] is not None
return cast(
Tuple[Dict, Dict],
PrimalSolutionComponent._extract(instance.features, sample),
PrimalSolutionComponent._extract(features, sample),
)
@staticmethod
@@ -227,7 +227,10 @@ class PrimalSolutionComponent(Component):
features: Features,
sample: TrainingSample,
) -> Dict:
return cast(Dict, PrimalSolutionComponent._extract(features, sample))
return cast(
Dict,
PrimalSolutionComponent._extract(features, sample),
)
@staticmethod
def _extract(

5
miplearn/features.py
View File

@@ -58,6 +58,7 @@ class FeaturesExtractor:
self,
instance: "Instance",
) -> Dict[str, ConstraintFeatures]:
has_static_lazy = instance.has_static_lazy_constraints()
constraints: Dict[str, ConstraintFeatures] = {}
for cid in self.solver.get_constraint_ids():
user_features = None
@@ -83,6 +84,10 @@ class FeaturesExtractor:
"Category": category,
"User features": user_features,
}
if has_static_lazy:
constraints[cid]["Lazy"] = instance.is_constraint_lazy(cid)
else:
constraints[cid]["Lazy"] = False
return constraints
@staticmethod

4
miplearn/instance.py
View File

@@ -101,13 +101,13 @@ class Instance(ABC):
def get_constraint_category(self, cid: str) -> Optional[str]:
return cid
def has_static_lazy_constraints(self):
def has_static_lazy_constraints(self) -> bool:
return False
def has_dynamic_lazy_constraints(self):
return False
def is_constraint_lazy(self, cid):
def is_constraint_lazy(self, cid: str) -> bool:
return False
def find_violated_lazy_constraints(self, model):

1
miplearn/types.py
View File

@@ -98,6 +98,7 @@ ConstraintFeatures = TypedDict(
"Sense": str,
"Category": Optional[Hashable],
"User features": Optional[List[float]],
"Lazy": bool,
},
total=False,
)

5
tests/components/test_component.py
View File

@@ -7,8 +7,7 @@ from miplearn import Component, Instance
def test_xy_instance():
def _xy_sample(instance, sample):
print(sample)
def _xy_sample(features, sample):
x = {
"s1": {
"category_a": [
@@ -54,8 +53,10 @@ def test_xy_instance():
comp = Component()
instance_1 = Mock(spec=Instance)
instance_1.training_data = ["s1", "s2"]
instance_1.features = {}
instance_2 = Mock(spec=Instance)
instance_2.training_data = ["s3"]
instance_2.features = {}
comp.xy_sample = _xy_sample
x_expected = {
"category_a": [

19
tests/components/test_lazy_static.py
View File

@@ -9,7 +9,7 @@ from miplearn.components.lazy_static import StaticLazyConstraintsComponent
from miplearn.instance import Instance
from miplearn.solvers.internal import InternalSolver
from miplearn.solvers.learning import LearningSolver
from miplearn.types import TrainingSample
from miplearn.types import TrainingSample, Features
def test_usage_with_solver():
@@ -234,32 +234,35 @@ def test_fit():
def test_xy_sample() -> None:
instance = Mock(spec=Instance)
sample: TrainingSample = {
"LazyStatic: Enforced": {"c1", "c2", "c4", "c5"},
"LazyStatic: All": {"c1", "c2", "c3", "c4", "c5"},
"LazyStatic: Enforced": {"c1", "c2", "c4"},
}
instance.features = {
features: Features = {
"Constraints": {
"c1": {
"Category": "type-a",
"User features": [1.0, 1.0],
"Lazy": True,
},
"c2": {
"Category": "type-a",
"User features": [1.0, 2.0],
"Lazy": True,
},
"c3": {
"Category": "type-a",
"User features": [1.0, 3.0],
"Lazy": True,
},
"c4": {
"Category": "type-b",
"User features": [1.0, 4.0, 0.0],
"Lazy": True,
},
"c5": {
"Category": "type-b",
"User features": [1.0, 5.0, 0.0],
"Lazy": False,
},
}
}
@@ -271,7 +274,6 @@ def test_xy_sample() -> None:
],
"type-b": [
[1.0, 4.0, 0.0],
[1.0, 5.0, 0.0],
],
}
y_expected = {
@@ -282,9 +284,10 @@ def test_xy_sample() -> None:
],
"type-b": [
[False, True],
[False, True],
],
}
x_actual, y_actual = StaticLazyConstraintsComponent.xy_sample(instance, sample)
xy = StaticLazyConstraintsComponent.xy_sample(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected

78
tests/components/test_objective.py
View File

@@ -11,35 +11,10 @@ from numpy.testing import assert_array_equal
from miplearn.instance import Instance
from miplearn.classifiers import Regressor
from miplearn.components.objective import ObjectiveValueComponent
from miplearn.types import TrainingSample
from miplearn.types import TrainingSample, Features
from tests.fixtures.knapsack import get_test_pyomo_instances
def test_xy_sample() -> None:
instance = cast(Instance, Mock(spec=Instance))
instance.features = {
"Instance": {
"User features": [1.0, 2.0],
}
}
sample: TrainingSample = {
"Lower bound": 1.0,
"Upper bound": 2.0,
"LP value": 3.0,
}
x_expected = {
"Lower bound": [[1.0, 2.0, 3.0]],
"Upper bound": [[1.0, 2.0, 3.0]],
}
y_expected = {
"Lower bound": [[1.0]],
"Upper bound": [[2.0]],
}
x_actual, y_actual = ObjectiveValueComponent.xy_sample(instance, sample)
assert x_actual == x_expected
assert y_actual == y_expected
def test_x_y_predict() -> None:
# Construct instance
instance = cast(Instance, Mock(spec=Instance))
@@ -125,3 +100,54 @@ def test_obj_evaluate():
"R2": -5.012843605607331,
},
}
def test_xy_sample_with_lp() -> None:
features: Features = {
"Instance": {
"User features": [1.0, 2.0],
}
}
sample: TrainingSample = {
"Lower bound": 1.0,
"Upper bound": 2.0,
"LP value": 3.0,
}
x_expected = {
"Lower bound": [[1.0, 2.0, 3.0]],
"Upper bound": [[1.0, 2.0, 3.0]],
}
y_expected = {
"Lower bound": [[1.0]],
"Upper bound": [[2.0]],
}
xy = ObjectiveValueComponent.xy_sample(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected
def test_xy_sample_without_lp() -> None:
features: Features = {
"Instance": {
"User features": [1.0, 2.0],
}
}
sample: TrainingSample = {
"Lower bound": 1.0,
"Upper bound": 2.0,
}
x_expected = {
"Lower bound": [[1.0, 2.0]],
"Upper bound": [[1.0, 2.0]],
}
y_expected = {
"Lower bound": [[1.0]],
"Upper bound": [[2.0]],
}
xy = ObjectiveValueComponent.xy_sample(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected

52
tests/components/test_primal.py
View File

@@ -1,22 +1,22 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import cast, List
from unittest.mock import Mock, call
from typing import cast
from unittest.mock import Mock
import numpy as np
from numpy.testing import assert_array_equal
from miplearn import Classifier
from miplearn.classifiers.threshold import Threshold, MinPrecisionThreshold
from miplearn.classifiers.threshold import Threshold
from miplearn.components.primal import PrimalSolutionComponent
from miplearn.instance import Instance
from miplearn.types import TrainingSample
from miplearn.types import TrainingSample, Features
def test_xy_sample_with_lp_solution() -> None:
instance = cast(Instance, Mock(spec=Instance))
instance.features = {
features: Features = {
"Variables": {
"x": {
0: {
@@ -56,34 +56,28 @@ def test_xy_sample_with_lp_solution() -> None:
},
}
x_expected = {
"default": np.array(
[
"default": [
[0.0, 0.0, 0.1],
[1.0, 0.0, 0.1],
[1.0, 1.0, 0.1],
]
)
}
y_expected = {
"default": np.array(
[
"default": [
[True, False],
[False, True],
[True, False],
]
)
}
x_actual, y_actual = PrimalSolutionComponent.xy_sample(instance, sample)
assert len(x_actual.keys()) == 1
assert len(y_actual.keys()) == 1
assert_array_equal(x_actual["default"], x_expected["default"])
assert_array_equal(y_actual["default"], y_expected["default"])
xy = PrimalSolutionComponent.xy_sample(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected
def test_xy_sample_without_lp_solution() -> None:
comp = PrimalSolutionComponent()
instance = cast(Instance, Mock(spec=Instance))
instance.features = {
features: Features = {
"Variables": {
"x": {
0: {
@@ -115,28 +109,24 @@ def test_xy_sample_without_lp_solution() -> None:
},
}
x_expected = {
"default": np.array(
[
"default": [
[0.0, 0.0],
[1.0, 0.0],
[1.0, 1.0],
]
)
}
y_expected = {
"default": np.array(
[
"default": [
[True, False],
[False, True],
[True, False],
]
)
}
x_actual, y_actual = comp.xy_sample(instance, sample)
assert len(x_actual.keys()) == 1
assert len(y_actual.keys()) == 1
assert_array_equal(x_actual["default"], x_expected["default"])
assert_array_equal(y_actual["default"], y_expected["default"])
xy = PrimalSolutionComponent.xy_sample(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected
def test_predict() -> None:

1
tests/test_features.py
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@@ -44,6 +44,7 @@ def test_knapsack() -> None:
},
"Sense": "<",
"RHS": 67.0,
"Lazy": False,
"Category": "eq_capacity",
"User features": [0.0],
}