Enhance exposure_spread function by refining replacement logic for players based on exposure targets. This update improves the handling of player replacements, ensuring that changes are only applied when necessary and maintaining clarity in the code structure.

global_func/exposure_spread.py

@@ -228,10 +228,8 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
     # 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:
-        print(f"Lineups to remove: {lineups_to_remove}")
-        print(f"Current Counter: {change_counter}")
-        if change_counter < math.ceil(lineups_to_remove):
-            for row in random_row_indices_insert:
+        for row in random_row_indices_insert:
+            if change_counter < math.ceil(lineups_to_remove):
                 if specific_replacements != []:
                     comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements)) &
                         (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row]))
@@ -267,8 +265,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
                 except:
                     comparable_player_list = []
 
-                if comparable_player_list != []:
-                    insert_player = random.choice(comparable_player_list)
+                if comparable_player_list:
                     # Find which column contains the exposure_player
                     if specific_columns != []:
                         row_data = working_frame.iloc[row][specific_columns]
@@ -277,23 +274,57 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
                         row_data = working_frame.iloc[row]
                         working_columns = working_frame.columns
                     
-                    for col in working_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):
+                    # Track if we successfully made a replacement
+                    replacement_made = False
+                    
+                    # For exposure_target == 0, replace ALL occurrences of exposure_player in this row
+                    if exposure_target == 0:
+                        for col in working_columns:
+                            if row_data[col] == exposure_player:
+                                # Try to find a suitable replacement for this specific column
+                                suitable_replacements = []
+                                for candidate in comparable_player_list:
+                                    # Get the replacement player's positions
+                                    replacement_player_positions = projections_df[projections_df['player_names'] == candidate]['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):
+                                            suitable_replacements.append(candidate)
+                                    else:
+                                        suitable_replacements.append(candidate)
+                                
+                                if suitable_replacements:
+                                    insert_player = random.choice(suitable_replacements)
                                     working_frame.at[row, col] = insert_player
+                                    replacement_made = True
+                                    # Remove this player from the list to avoid duplicates in the same row
+                                    comparable_player_list = [p for p in comparable_player_list if p != insert_player]
+                    else:
+                        # Original logic for non-zero exposure targets
+                        for col in working_columns:
+                            if row_data[col] == exposure_player:
+                                insert_player = random.choice(comparable_player_list)
+                                # 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
+                                        replacement_made = True
+                                        break
+                                else:
+                                    working_frame.at[row, col] = insert_player
+                                    replacement_made = True
                                     break
-                            else:
-                                working_frame.at[row, col] = insert_player
-                                break
-                    change_counter += 1
+                    
+                    # Only increment counter if we actually made a replacement
+                    if replacement_made:
+                        change_counter += 1
     else:
-        if change_counter < math.ceil(lineups_to_add):
-            for row in random_row_indices_replace:
+        for row in random_row_indices_replace:
+            if change_counter < math.ceil(lineups_to_add):
                 if specific_replacements != []:
                     comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements))
                     ]
@@ -326,7 +357,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
 
                 # Create a list of comparable players
                 comparable_player_list = comparable_players['player_names'].tolist()
-                if comparable_player_list != []:
+                if comparable_player_list:
                     # Find which column contains the exposure_player
                     if specific_columns != []:
                         row_data = working_frame.iloc[row][specific_columns]
@@ -353,6 +384,6 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
                                     change_counter += 1
                                     break
                             else:
-                                break
+                                continue
     return working_frame