Add ObjectiveValueComponent.xy

tests/components/test_objective.py

@@ -11,9 +11,33 @@ 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 tests.fixtures.knapsack import get_test_pyomo_instances
 
 
+def test_xy() -> None:
+    instance = cast(Instance, Mock(spec=Instance))
+    instance.get_instance_features = Mock(  # type: ignore
+        return_value=[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(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))

tests/components/test_primal.py

@@ -69,7 +69,7 @@ def test_xy_with_lp_solution() -> None:
             ]
         )
     }
-    x_actual, y_actual = comp.xy(instance, sample)
+    x_actual, y_actual = PrimalSolutionComponent.xy(instance, sample)
     assert len(x_actual.keys()) == 1
     assert len(y_actual.keys()) == 1
     assert_array_equal(x_actual["default"], x_expected["default"])