Add ObjectiveValueComponent.xy

miplearn/components/component.py

@@ -136,8 +136,8 @@ class Component(ABC):
     ) -> None:
         return
 
+    @staticmethod
     def xy(
-        self,
         instance: Any,
         training_sample: TrainingSample,
     ) -> Tuple[Dict, Dict]:

miplearn/components/objective.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from typing import List, Dict, Union, Callable, Optional, Any, TYPE_CHECKING
+from typing import List, Dict, Union, Callable, Optional, Any, TYPE_CHECKING, Tuple
 
 import numpy as np
 from sklearn.linear_model import LinearRegression
@@ -161,3 +161,21 @@ class ObjectiveValueComponent(Component):
             },
         }
         return ev
+
+    @staticmethod
+    def xy(
+        instance: Any,
+        sample: TrainingSample,
+    ) -> Tuple[Dict, Dict]:
+        x: Dict = {}
+        y: Dict = {}
+        if "Lower bound" not in sample:
+            return x, y
+        features = instance.get_instance_features()
+        if "LP value" in sample and sample["LP value"] is not None:
+            features += [sample["LP value"]]
+        x["Lower bound"] = [features]
+        x["Upper bound"] = [features]
+        y["Lower bound"] = [[sample["Lower bound"]]]
+        y["Upper bound"] = [[sample["Upper bound"]]]
+        return x, y

miplearn/components/primal.py

@@ -297,8 +297,8 @@ class PrimalSolutionComponent(Component):
         )
         return [opt_value < 0.5, opt_value > 0.5]
 
+    @staticmethod
     def xy(
-        self,
         instance: Any,
         sample: TrainingSample,
     ) -> Tuple[Dict, Dict]:

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"])