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Convert Features into dataclass

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This commit is contained in:
Alinson S. Xavier
2021-04-04 22:37:16 -05:00
parent f2520f33fb
commit 59f4f75a53
12 changed files with 77 additions and 67 deletions
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8
tests/components/test_lazy_static.py
View File

@@ -24,11 +24,11 @@ def sample() -> TrainingSample:
@pytest.fixture
def features() -> Features:
return {
"Instance": {
return Features(
instance={
"Lazy constraint count": 4,
},
"Constraints": {
constraints={
"c1": {
"Category": "type-a",
"User features": [1.0, 1.0],
@@ -55,7 +55,7 @@ def features() -> Features:
"Lazy": False,
},
},
}
)
def test_usage_with_solver(features: Features) -> None:

6
tests/components/test_objective.py
View File

@@ -17,11 +17,11 @@ import numpy as np
@pytest.fixture
def features() -> Features:
return {
"Instance": {
return Features(
instance={
"User features": [1.0, 2.0],
}
}
)
@pytest.fixture

24
tests/components/test_primal.py
View File

@@ -17,8 +17,8 @@ from miplearn.types import TrainingSample, Features
def test_xy() -> None:
features: Features = {
"Variables": {
features = Features(
variables={
"x": {
0: {
"Category": "default",
@@ -37,7 +37,7 @@ def test_xy() -> None:
},
}
}
}
)
sample: TrainingSample = {
"Solution": {
"x": {
@@ -78,8 +78,8 @@ def test_xy() -> None:
def test_xy_without_lp_solution() -> None:
features: Features = {
"Variables": {
features = Features(
variables={
"x": {
0: {
"Category": "default",
@@ -98,7 +98,7 @@ def test_xy_without_lp_solution() -> None:
},
}
}
}
)
sample: TrainingSample = {
"Solution": {
"x": {
@@ -143,8 +143,8 @@ def test_predict() -> None:
)
thr = Mock(spec=Threshold)
thr.predict = Mock(return_value=[0.75, 0.75])
features: Features = {
"Variables": {
features = Features(
variables={
"x": {
0: {
"Category": "default",
@@ -160,7 +160,7 @@ def test_predict() -> None:
},
}
}
}
)
sample: TrainingSample = {
"LP solution": {
"x": {
@@ -243,8 +243,8 @@ def test_evaluate() -> None:
4: 1.0,
}
}
features: Features = {
"Variables": {
features = Features(
variables={
"x": {
0: {},
1: {},
@@ -253,7 +253,7 @@ def test_evaluate() -> None:
4: {},
}
}
}
)
sample: TrainingSample = {
"Solution": {
"x": {