Rewrite ObjectiveValueComponent.sample_xy

miplearn/components/component.py

@@ -7,7 +7,7 @@ from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Hashable
 import numpy as np
 from overrides import EnforceOverrides
 
-from miplearn.features import TrainingSample, Features
+from miplearn.features import TrainingSample, Features, Sample
 from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 
@@ -119,6 +119,14 @@ class Component:
         """
         pass
 
+    def sample_xy(self, sample: Sample) -> Tuple[Dict, Dict]:
+        """
+        Returns a pair of x and y dictionaries containing, respectively, the matrices
+        of ML features and the labels for the sample. If the training sample does not
+        include label information, returns (x, {}).
+        """
+        pass
+
     def xy_instances(
         self,
         instances: List[Instance],

miplearn/components/objective.py

@@ -12,7 +12,7 @@ from sklearn.linear_model import LinearRegression
 from miplearn.classifiers import Regressor
 from miplearn.classifiers.sklearn import ScikitLearnRegressor
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Features
+from miplearn.features import TrainingSample, Features, Sample
 from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 
@@ -98,6 +98,39 @@ class ObjectiveValueComponent(Component):
             y["Upper bound"] = [[sample.upper_bound]]
         return x, y
 
+    @overrides
+    def sample_xy(
+        self,
+        sample: Sample,
+    ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
+        # Instance features
+        assert sample.after_load is not None
+        assert sample.after_load.instance is not None
+        f = sample.after_load.instance.to_list()
+
+        # LP solve features
+        if sample.after_lp is not None:
+            assert sample.after_lp.lp_solve is not None
+            f.extend(sample.after_lp.lp_solve.to_list())
+
+        # Features
+        x: Dict[Hashable, List[List[float]]] = {
+            "Upper bound": [f],
+            "Lower bound": [f],
+        }
+
+        # Labels
+        y: Dict[Hashable, List[List[float]]] = {}
+        if sample.after_mip is not None:
+            mip_stats = sample.after_mip.mip_solve
+            assert mip_stats is not None
+            if mip_stats.mip_lower_bound is not None:
+                y["Lower bound"] = [[mip_stats.mip_lower_bound]]
+            if mip_stats.mip_upper_bound is not None:
+                y["Upper bound"] = [[mip_stats.mip_upper_bound]]
+
+        return x, y
+
     @overrides
     def sample_evaluate_old(
         self,