Enhance player replacement logic in exposure_spread function by separating random row selection for insertion and replacement. This improves clarity and ensures accurate handling of lineups during exposure adjustments.

global_func/exposure_spread.py

@@ -246,8 +246,12 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
 
     # find the exposure rate of the player in the working frame
     player_mask = working_frame[working_frame.columns].apply(
-                                    lambda row: exposure_player in list(row), axis=1
-                                )
+        lambda row: exposure_player in list(row), axis=1
+    )
+    replace_mask = working_frame.apply(
+        lambda row: exposure_player not in list(row), axis=1
+    )
+
     player_exposure = player_mask.sum() / len(working_frame)
 
     # find the number of lineups that need to be removed to reach the target exposure
@@ -258,13 +262,17 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
 
     # isolate the rows that contain the player
     player_rows = working_frame[player_mask]
+    replace_rows = working_frame[replace_mask]
     if comparable_stack != 0:
         player_rows = player_rows[player_rows['Stack'] != comparable_stack]
+        replace_rows = replace_rows[replace_rows['Stack'] != comparable_stack]
     
     change_counter = 0
 
-    random_row_indices = list(player_rows.index)
-    random.shuffle(random_row_indices)
+    random_row_indices_insert = list(player_rows.index)
+    random_row_indices_replace = list(replace_rows.index)
+    random.shuffle(random_row_indices_insert)
+    random.shuffle(random_row_indices_replace)
 
     # for each row to the the number of lineups to remove, replace with random choice from comparable player list if they can be inserted
 
@@ -272,7 +280,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
     # key concept here is if they have a lineups to remove above 0 it means that we are trying to replace them with comparable players
     # if the lineups to remove is below zero it means we want to find comparable players and replace them with the exposure player
     if lineups_to_remove > 0:
-        for row in random_row_indices:
+        for row in random_row_indices_insert:
             if change_counter < math.ceil(lineups_to_remove):
                 comparable_players = projections_df[
                     (projections_df['salary'] >= comp_salary_low) &
@@ -319,7 +327,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
                     change_counter += 1
     else:
         lineups_to_remove = lineups_to_remove * -1
-        for row in random_row_indices:
+        for row in random_row_indices_replace:
             if change_counter < math.ceil(lineups_to_remove):
                 comparable_players = projections_df[
                     (projections_df['salary'] >= comp_salary_low) &
@@ -333,12 +341,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
                             lambda row: not any(team in list(row) for team in remove_teams), axis=1
                         )
                     comparable_players = comparable_players[remove_mask]
-                
-                remove_mask = working_frame.apply(
-                    lambda row: exposure_player not in list(row), axis=1
-                )
-                working_frame = working_frame[remove_mask]
-                
+
                 comparable_players = comparable_players[comparable_players['player_names'] != exposure_player]
 
                 # Create a list of comparable players