Refactor exposure_spread function to handle player replacement logic based on lineups_to_remove value. Separate logic for positive and negative adjustments to ensure accurate player exposure management and enhance lineup flexibility.

global_func/exposure_spread.py

@@ -267,50 +267,98 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
     random.shuffle(random_row_indices)
 
     # for each row to the the number of lineups to remove, replace with random choice from comparable player list if they can be inserted
-    for row in random_row_indices:
-        if change_counter < math.ceil(lineups_to_remove):
-            comparable_players = projections_df[
-                (projections_df['salary'] >= comp_salary_low) &
-                (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
-                (projections_df['median'] >= comp_projection_low) &
-                (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
-            ]
 
-            if exposure_target == 0:
-                comparable_players = comparable_players[comparable_players['player_names'] != exposure_player]
-            
-            if remove_teams is not None:
-                remove_mask = comparable_players.apply(
-                        lambda row: not any(team in list(row) for team in remove_teams), axis=1
-                    )
-                comparable_players = comparable_players[remove_mask]
+    # we will need to use two separate functions here, one for an exposure player who has a lineups to remove above 0 and one for below 0
+    # 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:
+            if change_counter < math.ceil(lineups_to_remove):
+                comparable_players = projections_df[
+                    (projections_df['salary'] >= comp_salary_low) &
+                    (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
+                    (projections_df['median'] >= comp_projection_low) &
+                    (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
+                ]
 
-            # Get the current row data to check for existing players
-            current_row_data = working_frame.iloc[row]
-            
-            # Filter out players that are already present in this row
-            existing_players = set(current_row_data.values)
-            comparable_players = comparable_players[~comparable_players['player_names'].isin(existing_players)]
+                if exposure_target == 0:
+                    comparable_players = comparable_players[comparable_players['player_names'] != exposure_player]
+                
+                if remove_teams is not None:
+                    remove_mask = comparable_players.apply(
+                            lambda row: not any(team in list(row) for team in remove_teams), axis=1
+                        )
+                    comparable_players = comparable_players[remove_mask]
 
-            # Create a list of comparable players
-            comparable_player_list = comparable_players['player_names'].tolist()
-            if comparable_player_list:
-                insert_player = random.choice(comparable_player_list)
-                # Find which column contains the exposure_player
-                row_data = working_frame.iloc[row]
-                for col in working_frame.columns:
-                    if row_data[col] == exposure_player:
-                        # Get the replacement player's positions
-                        replacement_player_positions = projections_df[projections_df['player_names'] == insert_player]['position'].iloc[0].split('/')
-                        
-                        # Check if the replacement player is eligible for this column
-                        if type_var == 'Classic':
-                            if check_position_eligibility(sport_var, col, replacement_player_positions):
+                # Get the current row data to check for existing players
+                current_row_data = working_frame.iloc[row]
+                
+                # Filter out players that are already present in this row
+                existing_players = set(current_row_data.values)
+                comparable_players = comparable_players[~comparable_players['player_names'].isin(existing_players)]
+
+                # Create a list of comparable players
+                comparable_player_list = comparable_players['player_names'].tolist()
+                if comparable_player_list:
+                    insert_player = random.choice(comparable_player_list)
+                    # Find which column contains the exposure_player
+                    row_data = working_frame.iloc[row]
+                    for col in working_frame.columns:
+                        if row_data[col] == exposure_player:
+                            # Get the replacement player's positions
+                            replacement_player_positions = projections_df[projections_df['player_names'] == insert_player]['position'].iloc[0].split('/')
+                            
+                            # Check if the replacement player is eligible for this column
+                            if type_var == 'Classic':
+                                if check_position_eligibility(sport_var, col, replacement_player_positions):
+                                    working_frame.at[row, col] = insert_player
+                                    break
+                            else:
                                 working_frame.at[row, col] = insert_player
                                 break
-                        else:
-                            working_frame.at[row, col] = insert_player
-                            break
-                change_counter += 1
+                    change_counter += 1
+    else:
+        for row in random_row_indices:
+            if change_counter < math.ceil(lineups_to_remove):
+                comparable_players = projections_df[
+                    (projections_df['salary'] >= comp_salary_low) &
+                    (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
+                    (projections_df['median'] >= comp_projection_low) &
+                    (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
+                ]
+                
+                if remove_teams is not None:
+                    remove_mask = comparable_players.apply(
+                            lambda row: not any(team in list(row) for team in remove_teams), axis=1
+                        )
+                    comparable_players = comparable_players[remove_mask]
+
+                # Get the current row data to check for existing players
+                current_row_data = working_frame.iloc[row]
+                
+                # Filter out players that are already present in this row
+                existing_players = set(current_row_data.values)
+                comparable_players = comparable_players[~comparable_players['player_names'].isin(existing_players)]
+
+                # Create a list of comparable players
+                comparable_player_list = comparable_players['player_names'].tolist()
+                if comparable_player_list:
+                    replace_player = random.choice(comparable_player_list)
+                    # Find which column contains the exposure_player
+                    row_data = working_frame.iloc[row]
+                    for col in working_frame.columns:
+                        if row_data[col] == replace_player:
+                            # Get the replacement player's positions
+                            replacement_player_positions = projections_df[projections_df['player_names'] == replace_player]['position'].iloc[0].split('/')
+                            
+                            # Check if the replacement player is eligible for this column
+                            if type_var == 'Classic':
+                                if check_position_eligibility(sport_var, col, replacement_player_positions):
+                                    working_frame.at[row, col] = exposure_player
+                                    break
+                            else:
+                                working_frame.at[row, col] = exposure_player
+                                break
+                    change_counter += 1
     return working_frame