Refactor large_field_preset function to streamline the portfolio selection process by removing unnecessary iterations and simplifying the logic for team-based filtering, enhancing efficiency and lineup accuracy.

global_func/large_field_preset.py

@@ -4,67 +4,24 @@ def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols
     
     for slack_var in range(1, 20):
         concat_portfolio = pd.DataFrame(columns=portfolio.columns)
-        
-        player_columns = [col for col in concat_portfolio.columns if col not in concat_portfolio]
-
-        remove_list = []
-
-        max_iterations = 5
-        for each_iteration in range(max_iterations):
-            player_stats = []
-            concat_portfolio = pd.DataFrame(columns=portfolio.columns)
-
-            for team in portfolio['Stack'].unique():
-                rows_to_drop = []
-                working_portfolio = portfolio.copy()
-                if remove_list:
-                    if len(remove_list) > 0:
-                        remove_mask = working_portfolio[player_columns].apply(
-                            lambda row: not any(player in list(row) for player in remove_list), axis=1
-                        )
-                        working_portfolio = working_portfolio[remove_mask]
-                            
-                working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Finish_percentile', ascending = True)
-                working_portfolio = working_portfolio.reset_index(drop=True)
-
-                if len(working_portfolio) == 0:
-                    continue
-                
-                curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
-
-                for i in range(1, len(working_portfolio)):
-                    if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
-                        rows_to_drop.append(i)
-                    else:
-                        curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
-                
-                working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
-                
-                concat_portfolio = pd.concat([concat_portfolio, working_portfolio])
-
-            player_names = set()
-            for col in concat_portfolio.columns:
-                if col not in exclude_cols:
-                    player_names.update(concat_portfolio[col].unique())
-            for player in player_names:
-                player_mask = concat_portfolio[player_columns].apply(
-                    lambda row: player in list(row), axis=1
-                )
-                
-                if player_mask.any():
-                    player_stats.append({
-                        'Player': player,
-                        'Lineup Count': player_mask.sum(),
-                        'Exposure': player_mask.sum() / len(concat_portfolio),
-                    })
-            player_exposure = pd.DataFrame(player_stats)
-            player_exposure = player_exposure[player_exposure['Exposure'] > .50]
-            remove_list = player_exposure['Player'].tolist()
-
-            if len(remove_list) == 0:
-                break
 
-        if len(concat_portfolio) >= lineup_target and len(remove_list) == 0:
+        for team in portfolio['Stack'].unique():
+            rows_to_drop = []
+            working_portfolio = portfolio.copy()
+            working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Finish_percentile', ascending = True)
+            working_portfolio = working_portfolio.reset_index(drop=True)
+            curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
+
+            for i in range(1, len(working_portfolio)):
+                if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
+                    rows_to_drop.append(i)
+                else:
+                    curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
+            
+            working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
+            concat_portfolio = pd.concat([concat_portfolio, working_portfolio])
+
+        if len(concat_portfolio) >= lineup_target:
             return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
         
     return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)