AlvLouWeh2017: Remove sample argument

miplearn/features/extractor.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from math import log, isfinite
-from typing import TYPE_CHECKING, List, Tuple
+from typing import TYPE_CHECKING, List, Tuple, Optional
 
 import numpy as np
 
@@ -74,7 +74,9 @@ class FeaturesExtractor:
             np.hstack(
                 [
                     vars_features_user,
-                    self._extract_var_features_AlvLouWeh2017(sample),
+                    self._extract_var_features_AlvLouWeh2017(
+                        obj_coeffs=variables.obj_coeffs,
+                    ),
                     variables.lower_bounds.reshape(-1, 1),
                     variables.obj_coeffs.reshape(-1, 1),
                     variables.upper_bounds.reshape(-1, 1),
@@ -111,7 +113,12 @@ class FeaturesExtractor:
         lp_var_features_list = []
         for f in [
             sample.get_array("static_var_features"),
-            self._extract_var_features_AlvLouWeh2017(sample),
+            self._extract_var_features_AlvLouWeh2017(
+                obj_coeffs=sample.get_array("static_var_obj_coeffs"),
+                obj_sa_up=variables.sa_obj_up,
+                obj_sa_down=variables.sa_obj_down,
+                values=variables.values,
+            ),
         ]:
             if f is not None:
                 lp_var_features_list.append(f)
@@ -310,12 +317,14 @@ class FeaturesExtractor:
 
     # Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
     # approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
-    def _extract_var_features_AlvLouWeh2017(self, sample: Sample) -> np.ndarray:
+    # noinspection PyPep8Naming
-        obj_coeffs = sample.get_array("static_var_obj_coeffs")
+    def _extract_var_features_AlvLouWeh2017(
-        obj_sa_down = sample.get_array("lp_var_sa_obj_down")
+        self,
-        obj_sa_up = sample.get_array("lp_var_sa_obj_up")
+        obj_coeffs: Optional[np.ndarray] = None,
-        values = sample.get_array("lp_var_values")
+        obj_sa_down: Optional[np.ndarray] = None,
-
+        obj_sa_up: Optional[np.ndarray] = None,
+        values: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
         assert obj_coeffs is not None
         obj_coeffs = obj_coeffs.astype(float)
         _fix_infinity(obj_coeffs)