minor stying and typing

recommendations/optimiser/CostOptimiser.py

@@ -1,4 +1,5 @@
 from mip import Model, xsum, minimize, BINARY, OptimizationStatus
+from typing import Mapping
 from utils.logger import setup_logger
 
 logger = setup_logger()
@@ -13,7 +14,11 @@ class CostOptimiser:
     BUFFER = 0.2
 
     def __init__(
-        self, components, min_gain, verbose=False, allow_slack=True
+        self,
+        components: list[list[Mapping[str, int | float | str]]],
+        min_gain: float | int,
+        verbose: bool = False,
+        allow_slack: bool = True
     ):
         self.components = components
         self.min_gain = min_gain

recommendations/optimiser/GainOptimiser.py

@@ -1,5 +1,6 @@
 from mip import Model, xsum, maximize, BINARY, OptimizationStatus
 from utils.logger import setup_logger
+from typing import Mapping
 
 logger = setup_logger()
 
@@ -9,7 +10,14 @@ class GainOptimiser:
     This class is used to maximise gain, given a constrained cost
     """
 
-    def __init__(self, components, max_cost, max_gain, allow_slack=True, verbose=False):
+    def __init__(
+        self,
+        components: list[list[Mapping[str, int | float | str]]],
+        max_cost: float | int,
+        max_gain: float | int,
+        allow_slack: bool = True,
+        verbose: bool = False
+    ):
         """
         This function will try and maximise the gain, given a constrained cost. If we specific a max_gain, then the
         optimisation routine is constained to try not to exceed a maximum increase

recommendations/optimiser/StrategicOptimiser.py

@@ -85,7 +85,7 @@ class StrategicOptimiser:
             # min cost
             # subject to
             #
-            # gain >= 𝐺
+            # gain >= G
             # cost <= B
             # multiple-choice constraints
             #