ANL-CEEESA/MIPLearn
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.git synced 2025-12-06 09:28:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

Objective: Use LP value as feature

Browse Source
This commit is contained in:
Alinson S. Xavier
2021-01-26 22:28:20 -06:00
parent fe47b0825f
commit 9abcea05cd
2 changed files with 5 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
miplearn/components/objective.py
View File

@@ -108,9 +108,8 @@ class ObjectiveValueComponent(Component):
def x(instances: Union[List[str], List[Instance]]) -> np.ndarray:
result = []
for instance in InstanceIterator(instances):
for _ in instance.training_data:
instance_features = instance.get_instance_features()
result.append(instance_features)
for sample in instance.training_data:
result.append(instance.get_instance_features() + [sample["LP value"]])
return np.array(result)
@staticmethod

4
tests/components/test_objective.py
View File

@@ -23,10 +23,12 @@ def test_x_y_predict() -> None:
{
"Lower bound": 1.0,
"Upper bound": 2.0,
"LP value": 3.0,
},
{
"Lower bound": 1.5,
"Upper bound": 2.2,
"LP value": 3.4,
},
]
@@ -41,7 +43,7 @@ def test_x_y_predict() -> None:
)
# Should build x correctly
x_expected = np.array([[1.0, 2.0], [1.0, 2.0]])
x_expected = np.array([[1.0, 2.0, 3.0], [1.0, 2.0, 3.4]])
assert_array_equal(comp.x([instance]), x_expected)
# Should build y correctly