Make get_instance_features return np.ndarray

miplearn/features/extractor.py

@@ -261,11 +261,18 @@ class FeaturesExtractor:
         instance: "Instance",
         sample: Sample,
     ) -> None:
-        features = cast(np.ndarray, instance.get_instance_features())
-        if isinstance(features, list):
-            features = np.array(features, dtype=float)
-        assert isinstance(features, np.ndarray)
-        assert features.dtype.kind in ["f"], f"Unsupported dtype: {features.dtype}"
+        features = instance.get_instance_features()
+        assert isinstance(features, np.ndarray), (
+            f"Instance features must be a numpy array. "
+            f"Found {features.__class__} instead."
+        )
+        assert len(features.shape) == 1, (
+            f"Instance features must be a vector. "
+            f"Found array with shape {features.shape} instead."
+        )
+        assert features.dtype.kind in [
+            "f"
+        ], f"Instance features have unsupported dtype: {features.dtype}"
         sample.put_array("static_instance_features", features)
 
     # Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based

miplearn/instance/base.py

@@ -6,6 +6,8 @@ import logging
 from abc import ABC, abstractmethod
 from typing import Any, List, TYPE_CHECKING, Dict
 
+import numpy as np
+
 from miplearn.features.sample import Sample, MemorySample
 
 logger = logging.getLogger(__name__)
@@ -37,7 +39,7 @@ class Instance(ABC):
         """
         pass
 
-    def get_instance_features(self) -> List[float]:
+    def get_instance_features(self) -> np.ndarray:
         """
         Returns a 1-dimensional array of (numerical) features describing the
         entire instance.
@@ -59,7 +61,7 @@ class Instance(ABC):
 
         By default, returns [0.0].
         """
-        return [0.0]
+        return np.zeros(1)
 
     def get_variable_features(self) -> Dict[str, List[float]]:
         """

miplearn/instance/file.py

@@ -6,6 +6,7 @@ import os
 from typing import Any, Optional, List, Dict, TYPE_CHECKING
 import pickle
 
+import numpy as np
 from overrides import overrides
 
 from miplearn.features.sample import Hdf5Sample, Sample
@@ -30,7 +31,7 @@ class FileInstance(Instance):
         return self.instance.to_model()
 
     @overrides
-    def get_instance_features(self) -> List[float]:
+    def get_instance_features(self) -> np.ndarray:
         assert self.instance is not None
         return self.instance.get_instance_features()
 

miplearn/instance/picklegz.py

@@ -8,6 +8,7 @@ import os
 import pickle
 from typing import Optional, Any, List, cast, IO, TYPE_CHECKING, Dict
 
+import numpy as np
 from overrides import overrides
 
 from miplearn.features.sample import Sample
@@ -42,7 +43,7 @@ class PickleGzInstance(Instance):
         return self.instance.to_model()
 
     @overrides
-    def get_instance_features(self) -> List[float]:
+    def get_instance_features(self) -> np.ndarray:
         assert self.instance is not None
         return self.instance.get_instance_features()
 

miplearn/problems/knapsack.py

@@ -94,8 +94,8 @@ class MultiKnapsackInstance(Instance):
         return model
 
     @overrides
-    def get_instance_features(self) -> List[float]:
-        return [float(np.mean(self.prices))] + list(self.capacities)
+    def get_instance_features(self) -> np.ndarray:
+        return np.array([float(np.mean(self.prices))] + list(self.capacities))
 
     @overrides
     def get_variable_features(self) -> Dict[str, List[float]]:

miplearn/solvers/pyomo/base.py

@@ -622,11 +622,13 @@ class PyomoTestInstanceKnapsack(Instance):
         return model
 
     @overrides
-    def get_instance_features(self) -> List[float]:
-        return [
+    def get_instance_features(self) -> np.ndarray:
+        return np.array(
+            [
                 self.capacity,
                 np.average(self.weights),
             ]
+        )
 
     @overrides
     def get_variable_features(self) -> Dict[str, List[float]]: