relocate min_max_scale function to utils

assume/common/utils.py

@@ -682,3 +682,18 @@ def calculate_content_size(content: list | dict) -> int:
             calculate_content_size(item) for item in content
         )
     return sys.getsizeof(content)
+
+
+def min_max_scale(x, min_val: float, max_val: float):
+    """
+    Min-Max scaling of a value x to the range [0, 1]
+
+    Args:
+        x: value(s) to scale
+        min_val: minimum value of the parameter
+        max_val: maximum value of the parameter
+    """
+    # Avoid division by zero
+    if min_val == max_val:
+        return x
+    return (x - min_val) / (max_val - min_val)

assume/reinforcement_learning/learning_utils.py

@@ -104,21 +104,6 @@ def polyak_update(params, target_params, tau: float):
             th.add(target_param.data, param.data, alpha=tau, out=target_param.data)
 
 
-def min_max_scale(x, min_val: float, max_val: float):
-    """
-    Min-Max scaling of a value x to the range [0, 1]
-
-    Args:
-        x: value(s) to scale
-        min_val: minimum value of the parameter
-        max_val: maximum value of the parameter
-    """
-    # Avoid division by zero
-    if min_val == max_val:
-        return x
-    return (x - min_val) / (max_val - min_val)
-
-
 def linear_schedule_func(
     start: float, end: float = 0, end_fraction: float = 1
 ) -> Schedule:

assume/strategies/learning_advanced_orders.py

@@ -9,8 +9,7 @@
 
 from assume.common.base import SupportsMinMax
 from assume.common.market_objects import MarketConfig, Orderbook, Product
-from assume.common.utils import get_products_index
-from assume.reinforcement_learning.learning_utils import min_max_scale
+from assume.common.utils import get_products_index, min_max_scale
 from assume.strategies.learning_strategies import RLStrategy
 
 

assume/strategies/learning_strategies.py

@@ -11,11 +11,9 @@
 
 from assume.common.base import LearningStrategy, SupportsMinMax, SupportsMinMaxCharge
 from assume.common.market_objects import MarketConfig, Orderbook, Product
+from assume.common.utils import min_max_scale
 from assume.reinforcement_learning.algorithms import actor_architecture_aliases
-from assume.reinforcement_learning.learning_utils import (
-    NormalActionNoise,
-    min_max_scale,
-)
+from assume.reinforcement_learning.learning_utils import NormalActionNoise
 
 logger = logging.getLogger(__name__)