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Convert VariableFeatures into dataclass

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This commit is contained in:
Alinson S. Xavier
2021-04-04 22:56:26 -05:00
parent 59f4f75a53
commit d79eec5da6
6 changed files with 97 additions and 87 deletions
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8
miplearn/components/primal.py
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@@ -136,7 +136,7 @@ class PrimalSolutionComponent(Component):
category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
for (var_name, var_dict) in features.variables.items():
for (idx, var_features) in var_dict.items():
category = var_features["Category"]
category = var_features.category
offset = category_offset[category]
category_offset[category] += 1
if y_pred[category][offset, 0]:
@@ -159,15 +159,15 @@ class PrimalSolutionComponent(Component):
solution = sample["Solution"]
for (var_name, var_dict) in features.variables.items():
for (idx, var_features) in var_dict.items():
category = var_features["Category"]
category = var_features.category
if category is None:
continue
if category not in x.keys():
x[category] = []
y[category] = []
f: List[float] = []
assert var_features["User features"] is not None
f += var_features["User features"]
assert var_features.user_features is not None
f += var_features.user_features
if "LP solution" in sample and sample["LP solution"] is not None:
lp_value = sample["LP solution"][var_name][idx]
if lp_value is not None:

31
miplearn/features.py
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@@ -4,9 +4,15 @@
import numbers
import collections
from typing import TYPE_CHECKING, Dict
from typing import TYPE_CHECKING, Dict, Hashable
from miplearn.types import Features, ConstraintFeatures, InstanceFeatures
from miplearn.types import (
Features,
ConstraintFeatures,
InstanceFeatures,
VariableFeatures,
VarIndex,
)
if TYPE_CHECKING:
from miplearn import InternalSolver, Instance
@@ -24,9 +30,14 @@ class FeaturesExtractor:
instance.features.constraints = self._extract_constraints(instance)
instance.features.instance = self._extract_instance(instance, instance.features)
def _extract_variables(self, instance: "Instance") -> Dict:
variables = self.solver.get_empty_solution()
for (var_name, var_dict) in variables.items():
def _extract_variables(
self,
instance: "Instance",
) -> Dict[str, Dict[VarIndex, VariableFeatures]]:
result: Dict[str, Dict[VarIndex, VariableFeatures]] = {}
empty_solution = self.solver.get_empty_solution()
for (var_name, var_dict) in empty_solution.items():
result[var_name] = {}
for idx in var_dict.keys():
user_features = None
category = instance.get_variable_category(var_name, idx)
@@ -47,11 +58,11 @@ class FeaturesExtractor:
f"Found {type(v).__name__} instead "
f"for var={var_name}[{idx}]."
)
var_dict[idx] = {
"Category": category,
"User features": user_features,
}
return variables
result[var_name][idx] = VariableFeatures(
category=category,
user_features=user_features,
)
return result
def _extract_constraints(
self,

2
miplearn/solvers/internal.py
View File

@@ -274,7 +274,7 @@ class InternalSolver(ABC):
pass
@abstractmethod
def get_empty_solution(self) -> Dict:
def get_empty_solution(self) -> Dict[str, Dict[VarIndex, Optional[float]]]:
"""
Returns a dictionary with the same shape as the one produced by
`get_solution`, but with all values set to None. This method is

14
miplearn/types.py
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@@ -87,14 +87,12 @@ InstanceFeatures = TypedDict(
total=False,
)
VariableFeatures = TypedDict(
"VariableFeatures",
{
"Category": Optional[Hashable],
"User features": Optional[List[float]],
},
total=False,
)
@dataclass
class VariableFeatures:
category: Optional[Hashable] = None
user_features: Optional[List[float]] = None
ConstraintFeatures = TypedDict(
"ConstraintFeatures",

96
tests/components/test_primal.py
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@@ -13,28 +13,28 @@ from miplearn.classifiers.threshold import Threshold
from miplearn.components import classifier_evaluation_dict
from miplearn.components.primal import PrimalSolutionComponent
from miplearn.problems.tsp import TravelingSalesmanGenerator
from miplearn.types import TrainingSample, Features
from miplearn.types import TrainingSample, Features, VariableFeatures
def test_xy() -> None:
features = Features(
variables={
"x": {
0: {
"Category": "default",
"User features": [0.0, 0.0],
},
1: {
"Category": None,
},
2: {
"Category": "default",
"User features": [1.0, 0.0],
},
3: {
"Category": "default",
"User features": [1.0, 1.0],
},
0: VariableFeatures(
category="default",
user_features=[0.0, 0.0],
),
1: VariableFeatures(
category=None,
),
2: VariableFeatures(
category="default",
user_features=[1.0, 0.0],
),
3: VariableFeatures(
category="default",
user_features=[1.0, 1.0],
),
}
}
)
@@ -81,21 +81,21 @@ def test_xy_without_lp_solution() -> None:
features = Features(
variables={
"x": {
0: {
"Category": "default",
"User features": [0.0, 0.0],
},
1: {
"Category": None,
},
2: {
"Category": "default",
"User features": [1.0, 0.0],
},
3: {
"Category": "default",
"User features": [1.0, 1.0],
},
0: VariableFeatures(
category="default",
user_features=[0.0, 0.0],
),
1: VariableFeatures(
category=None,
),
2: VariableFeatures(
category="default",
user_features=[1.0, 0.0],
),
3: VariableFeatures(
category="default",
user_features=[1.0, 1.0],
),
}
}
)
@@ -146,18 +146,18 @@ def test_predict() -> None:
features = Features(
variables={
"x": {
0: {
"Category": "default",
"User features": [0.0, 0.0],
},
1: {
"Category": "default",
"User features": [0.0, 2.0],
},
2: {
"Category": "default",
"User features": [2.0, 0.0],
},
0: VariableFeatures(
category="default",
user_features=[0.0, 0.0],
),
1: VariableFeatures(
category="default",
user_features=[0.0, 2.0],
),
2: VariableFeatures(
category="default",
user_features=[2.0, 0.0],
),
}
}
)
@@ -246,11 +246,11 @@ def test_evaluate() -> None:
features = Features(
variables={
"x": {
0: {},
1: {},
2: {},
3: {},
4: {},
0: VariableFeatures(),
1: VariableFeatures(),
2: VariableFeatures(),
3: VariableFeatures(),
4: VariableFeatures(),
}
}
)

33
tests/test_features.py
View File

@@ -4,6 +4,7 @@
from miplearn import GurobiSolver
from miplearn.features import FeaturesExtractor
from miplearn.types import VariableFeatures
from tests.fixtures.knapsack import get_knapsack_instance
@@ -16,22 +17,22 @@ def test_knapsack() -> None:
FeaturesExtractor(solver).extract(instance)
assert instance.features.variables == {
"x": {
0: {
"Category": "default",
"User features": [23.0, 505.0],
},
1: {
"Category": "default",
"User features": [26.0, 352.0],
},
2: {
"Category": "default",
"User features": [20.0, 458.0],
},
3: {
"Category": "default",
"User features": [18.0, 220.0],
},
0: VariableFeatures(
category="default",
user_features=[23.0, 505.0],
),
1: VariableFeatures(
category="default",
user_features=[26.0, 352.0],
),
2: VariableFeatures(
category="default",
user_features=[20.0, 458.0],
),
3: VariableFeatures(
category="default",
user_features=[18.0, 220.0],
),
}
}
assert instance.features.constraints == {