Implement InstanceFeaturesExtractor and ObjectiveValueExtractor

miplearn/__init__.py

@@ -12,6 +12,8 @@ from .components.branching import BranchPriorityComponent
 from .extractors import (UserFeaturesExtractor,
                          SolutionExtractor,
                          CombinedExtractor,
+                         InstanceFeaturesExtractor,
+                         ObjectiveValueExtractor,
                         )
 from .benchmark import BenchmarkRunner
 from .instance import Instance

miplearn/extractors.py

@@ -103,3 +103,28 @@ class CombinedExtractor(Extractor):
     def extract(self, instances, models):
         return self.merge([ex.extract(instances, models)
                            for ex in self.extractors])
+
+    
+class InstanceFeaturesExtractor(Extractor):
+    def extract(self, instances, models=None):
+        return np.vstack([
+            np.hstack([
+                instance.get_instance_features(),
+                instance.lp_value,
+            ])
+            for instance in instances
+        ])
+    
+    
+class ObjectiveValueExtractor(Extractor):
+    def __init__(self, kind="lp"):
+        assert kind in ["lower bound", "upper bound", "lp"]
+        self.kind = kind
+        
+    def extract(self, instances, models=None):
+        if self.kind == "lower bound":
+            return np.array([[instance.lower_bound] for instance in instances])
+        if self.kind == "upper bound":
+            return np.array([[instance.upper_bound] for instance in instances])
+        if self.kind == "lp":
+            return np.array([[instance.lp_value] for instance in instances])

miplearn/tests/test_extractors.py

@@ -7,13 +7,14 @@ from miplearn import (LearningSolver,
                       UserFeaturesExtractor,
                       SolutionExtractor,
                       CombinedExtractor,
+                      InstanceFeaturesExtractor
                      )
 import numpy as np
 import pyomo.environ as pe
 
 
 def _get_instances():
-    return [
+    instances = [
         KnapsackInstance(weights=[1., 2., 3.],
                          prices=[10., 20., 30.],
                          capacity=2.5,
@@ -23,10 +24,15 @@ def _get_instances():
                          capacity=4.5,
                         ),
     ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    return instances, models
 
 
-def test_user_features():
+def test_user_features_extractor():
-    instances = _get_instances()
+    instances, models = _get_instances()
     extractor = UserFeaturesExtractor()
     features = extractor.extract(instances)
     assert isinstance(features, dict)
@@ -36,12 +42,7 @@ def test_user_features():
 
     
 def test_solution_extractor():
-    instances = _get_instances()
+    instances, models = _get_instances()
-    models = [instance.to_model() for instance in instances]
-    solver = LearningSolver()
-    for (i, instance) in enumerate(instances):
-        solver.solve(instances[i], models[i])
-    
     features = SolutionExtractor().extract(instances, models)
     assert isinstance(features, dict)
     assert "default" in features.keys()
@@ -58,12 +59,7 @@ def test_solution_extractor():
 
     
 def test_combined_extractor():
-    instances = _get_instances()
+    instances, models = _get_instances()
-    models = [instance.to_model() for instance in instances]
-    solver = LearningSolver()
-    for (i, instance) in enumerate(instances):
-        solver.solve(instances[i], models[i])
-    
     extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(),
                                               SolutionExtractor()])
     features = extractor.extract(instances, models)
@@ -72,3 +68,8 @@ def test_combined_extractor():
     assert isinstance(features["default"], np.ndarray)
     assert features["default"].shape == (6, 6)
     
+    
+def test_instance_features_extractor():
+    instances, models = _get_instances()
+    features = InstanceFeaturesExtractor().extract(instances)
+    assert features.shape == (2,3)