Enhance small_field_preset and large_field_preset functions to accept an additional parameter for excluded columns, improving filtering capabilities and lineup accuracy. Update app.py to reflect these changes in preset selections.

app.py

@@ -1112,9 +1112,9 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         if preset_choice == 'Small Field (Heavy Own)':
-                            parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target)
+                            parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
                         elif preset_choice == 'Large Field (Finish Percentile / Edge)':
-                            parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target)
+                            parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
                         # elif preset_choice == 'Volatile':
                         #     parsed_frame = volatile_preset(st.session_state['working_frame'], lineup_target)
                         # elif preset_choice == 'Distributed':

global_func/large_field_preset.py

@@ -1,26 +1,70 @@
 import pandas as pd
 
-def large_field_preset(portfolio: pd.DataFrame, lineup_target: int):
+def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
     
     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
 
-        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:
+        if len(concat_portfolio) >= lineup_target and len(remove_list) == 0:
             return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
         
     return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)

global_func/small_field_preset.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-def small_field_preset(portfolio: pd.DataFrame, lineup_target: int):
+def small_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
 
     for slack_var in range(1, 20):
         concat_portfolio = pd.DataFrame(columns=portfolio.columns)