Refactor lineup generation logic in hedging_preset.py to improve processing efficiency. Introduced an initialization counter for lineup adjustments and added checks for empty DataFrames before generating lineups. Enhanced debug messages for better visibility into the lineup concatenation process.

global_func/hedging_preset.py

@@ -33,51 +33,59 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
 
     team_ownership = get_team_hitter_ownership(projections_file)
     top_owned_teams = team_ownership.head(list_size).index.tolist()
+    init_counter = 6
 
-    concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+    for runs in range(1, 5):
+        concat_portfolio = pd.DataFrame(columns=portfolio.columns)
+        for player in top_owned:
+            print(player)
+            working_df = portfolio.copy()
 
-    for player in top_owned:
-        print(player)
-        working_df = portfolio.copy()
+            # Create mask for lineups that contain any of the removed players
+            player_columns = [col for col in working_df.columns if col not in excluded_cols]
 
-        # Create mask for lineups that contain any of the removed players
-        player_columns = [col for col in working_df.columns if col not in excluded_cols]
+            remove_mask = working_df[player_columns].apply(
+                lambda row: player not in list(row), axis=1
+            )
+            lock_mask = working_df[player_columns].apply(
+                lambda row: player in list(row), axis=1
+            )
 
-        remove_mask = working_df[player_columns].apply(
-            lambda row: player not in list(row), axis=1
-        )
-        lock_mask = working_df[player_columns].apply(
-            lambda row: player in list(row), axis=1
-        )
+            removed_df = working_df[remove_mask]
+            locked_df = working_df[lock_mask]
 
-        removed_df = working_df[remove_mask]
-        locked_df = working_df[lock_mask]
+            removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
+            print(len(removed_lineups))
+            # Check if locked_df is empty before calling large_field_preset
+            if not locked_df.empty:
+                locked_lineups = large_field_preset(locked_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
+                print(len(locked_lineups))
+                concat_portfolio = pd.concat([concat_portfolio, removed_lineups, locked_lineups])
+            else:
+                # If no lineups contain this player, just add the removed lineups
+                print(f"No lineups found containing {player}")
+                concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
+        
+        for team in top_owned_teams:
+            working_df = portfolio.copy()
+            removed_df = working_df[working_df['Stack'] != team]
+            teams_df = working_df[working_df['Stack'] == team]
 
-        removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * 4)), excluded_cols)
-        print(len(removed_lineups))
-        # Check if locked_df is empty before calling large_field_preset
-        if not locked_df.empty:
-            locked_lineups = large_field_preset(locked_df, math.ceil(lineup_target / (list_size * 4)), excluded_cols)
-            print(len(locked_lineups))
-            concat_portfolio = pd.concat([concat_portfolio, removed_lineups, locked_lineups])
-        else:
-            # If no lineups contain this player, just add the removed lineups
-            print(f"No lineups found containing {player}")
-            concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
-    
-    for team in top_owned_teams:
-        working_df = portfolio.copy()
-        removed_df = working_df[working_df['Stack'] != team]
-        teams_df = working_df[working_df['Stack'] == team]
+            removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
+            # Check if teams_df is empty before calling large_field_preset
+            if not teams_df.empty:
+                team_lineups = large_field_preset(teams_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
+                concat_portfolio = pd.concat([concat_portfolio, removed_lineups, team_lineups])
+            else:
+                # If no lineups have this team stacked, just add the removed lineups
+                print(f"No lineups found with {team} stacked")
+                concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
+
+        concat_portfolio = concat_portfolio.drop_duplicates(subset=['median', 'Own', 'Lineup Edge', 'Similarity Score'])
 
-        removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * 4)), excluded_cols)
-        # Check if teams_df is empty before calling large_field_preset
-        if not teams_df.empty:
-            team_lineups = large_field_preset(teams_df, math.ceil(lineup_target / (list_size * 4)), excluded_cols)
-            concat_portfolio = pd.concat([concat_portfolio, removed_lineups, team_lineups])
+        if len(concat_portfolio) >= lineup_target:
+            return concat_portfolio.head(lineup_target)
         else:
-            # If no lineups have this team stacked, just add the removed lineups
-            print(f"No lineups found with {team} stacked")
-            concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
+            init_counter -= 1
 
     return concat_portfolio.head(lineup_target)