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Use compact variable features everywhere

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This commit is contained in:
Alinson S. Xavier
2021-04-15 09:49:35 -05:00
parent fec0113722
commit 95e326f5f6
11 changed files with 147 additions and 374 deletions
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56
tests/components/test_primal.py
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@@ -13,10 +13,10 @@ from miplearn.classifiers.threshold import Threshold
from miplearn.components import classifier_evaluation_dict
from miplearn.components.primal import PrimalSolutionComponent
from miplearn.features import (
Variable,
Features,
Sample,
InstanceFeatures,
VariableFeatures,
)
from miplearn.problems.tsp import TravelingSalesmanGenerator
from miplearn.solvers.learning import LearningSolver
@@ -28,39 +28,37 @@ def sample() -> Sample:
sample = Sample(
after_load=Features(
instance=InstanceFeatures(),
variables_old={
"x[0]": Variable(category="default"),
"x[1]": Variable(category=None),
"x[2]": Variable(category="default"),
"x[3]": Variable(category="default"),
},
variables=VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]"),
categories=("default", None, "default", "default"),
),
),
after_lp=Features(
variables_old={
"x[0]": Variable(),
"x[1]": Variable(),
"x[2]": Variable(),
"x[3]": Variable(),
},
variables=VariableFeatures(),
),
after_mip=Features(
variables_old={
"x[0]": Variable(value=0.0),
"x[1]": Variable(value=1.0),
"x[2]": Variable(value=1.0),
"x[3]": Variable(value=0.0),
}
variables=VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]"),
values=(0.0, 1.0, 1.0, 0.0),
)
),
)
sample.after_load.instance.to_list = Mock(return_value=[5.0]) # type: ignore
sample.after_lp.variables_old["x[0]"].to_list = Mock( # type: ignore
return_value=[0.0, 0.0]
sample.after_load.variables.to_list = Mock( # type:ignore
side_effect=lambda i: [
[0.0, 0.0],
None,
[1.0, 0.0],
[1.0, 1.0],
][i]
)
sample.after_lp.variables_old["x[2]"].to_list = Mock( # type: ignore
return_value=[1.0, 0.0]
)
sample.after_lp.variables_old["x[3]"].to_list = Mock( # type: ignore
return_value=[1.0, 1.0]
sample.after_lp.variables.to_list = Mock( # type:ignore
side_effect=lambda i: [
[2.0, 2.0],
None,
[3.0, 2.0],
[3.0, 3.0],
][i]
)
return sample
@@ -68,9 +66,9 @@ def sample() -> Sample:
def test_xy(sample: Sample) -> None:
x_expected = {
"default": [
[5.0, 0.0, 0.0],
[5.0, 1.0, 0.0],
[5.0, 1.0, 1.0],
[5.0, 0.0, 0.0, 2.0, 2.0],
[5.0, 1.0, 0.0, 3.0, 2.0],
[5.0, 1.0, 1.0, 3.0, 3.0],
]
}
y_expected = {