Objective: Rewrite sample_evaluate

miplearn/components/__init__.py

@@ -4,7 +4,12 @@
 from typing import Dict
 
 
-def classifier_evaluation_dict(tp: int, tn: int, fp: int, fn: int) -> Dict:
+def classifier_evaluation_dict(
+    tp: int,
+    tn: int,
+    fp: int,
+    fn: int,
+) -> Dict[str, float]:
     p = tp + fn
     n = fp + tn
     d: Dict = {

miplearn/components/component.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import numpy as np
-from typing import Any, List, Union, TYPE_CHECKING, Tuple, Dict, Optional
+from typing import Any, List, Union, TYPE_CHECKING, Tuple, Dict, Optional, Hashable
 
 from miplearn.extractors import InstanceIterator
 from miplearn.instance import Instance
@@ -205,5 +205,9 @@ class Component:
                 ev += [self.sample_evaluate(instance.features, sample)]
         return ev
 
-    def sample_evaluate(self, features: Features, sample: TrainingSample) -> Dict:
+    def sample_evaluate(
+        self,
+        features: Features,
+        sample: TrainingSample,
+    ) -> Dict[Hashable, Dict[str, float]]:
         return {}

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, Optional, Any, TYPE_CHECKING, Tuple
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING, Tuple, Hashable
 
 import numpy as np
 from sklearn.linear_model import LinearRegression
@@ -149,3 +149,25 @@ class ObjectiveValueComponent(Component):
         if "Upper bound" in sample and sample["Upper bound"] is not None:
             y["Upper bound"] = [[sample["Upper bound"]]]
         return x, y
+
+    def sample_evaluate(
+        self,
+        features: Features,
+        sample: TrainingSample,
+    ) -> Dict[Hashable, Dict[str, float]]:
+        def compare(y_pred: float, y_actual: float) -> Dict[str, float]:
+            err = np.round(abs(y_pred - y_actual), 8)
+            return {
+                "Actual value": y_actual,
+                "Predicted value": y_pred,
+                "Absolute error": err,
+                "Relative error": err / y_actual,
+            }
+
+        result: Dict[Hashable, Dict[str, float]] = {}
+        pred = self.sample_predict(features, sample)
+        if "Upper bound" in sample and sample["Upper bound"] is not None:
+            result["Upper bound"] = compare(pred["Upper bound"], sample["Upper bound"])
+        if "Lower bound" in sample and sample["Lower bound"] is not None:
+            result["Lower bound"] = compare(pred["Lower bound"], sample["Lower bound"])
+        return result

miplearn/components/primal.py

@@ -186,7 +186,7 @@ class PrimalSolutionComponent(Component):
         self,
         features: Features,
         sample: TrainingSample,
-    ) -> Dict:
+    ) -> Dict[Hashable, Dict[str, float]]:
         solution_actual = sample["Solution"]
         assert solution_actual is not None
         solution_pred = self.sample_predict(features, sample)