Make xy_sample receive features, not instances

tests/components/test_objective.py

@@ -11,35 +11,10 @@ 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 miplearn.types import TrainingSample, Features
 from tests.fixtures.knapsack import get_test_pyomo_instances
 
 
-def test_xy_sample() -> None:
-    instance = cast(Instance, Mock(spec=Instance))
-    instance.features = {
-        "Instance": {
-            "User features": [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_sample(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))
@@ -125,3 +100,54 @@ def test_obj_evaluate():
             "R2": -5.012843605607331,
         },
     }
+
+
+def test_xy_sample_with_lp() -> None:
+    features: Features = {
+        "Instance": {
+            "User features": [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]],
+    }
+    xy = ObjectiveValueComponent.xy_sample(features, sample)
+    assert xy is not None
+    x_actual, y_actual = xy
+    assert x_actual == x_expected
+    assert y_actual == y_expected
+
+
+def test_xy_sample_without_lp() -> None:
+    features: Features = {
+        "Instance": {
+            "User features": [1.0, 2.0],
+        }
+    }
+    sample: TrainingSample = {
+        "Lower bound": 1.0,
+        "Upper bound": 2.0,
+    }
+    x_expected = {
+        "Lower bound": [[1.0, 2.0]],
+        "Upper bound": [[1.0, 2.0]],
+    }
+    y_expected = {
+        "Lower bound": [[1.0]],
+        "Upper bound": [[2.0]],
+    }
+    xy = ObjectiveValueComponent.xy_sample(features, sample)
+    assert xy is not None
+    x_actual, y_actual = xy
+    assert x_actual == x_expected
+    assert y_actual == y_expected