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import pandas as pd
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)
# Define player columns (columns that contain player names)
player_columns = [col for col in portfolio.columns if col not in exclude_cols]
# Track players to remove across iterations
remove_list = []
# Iterate until no high-exposure players are found
max_iterations = 10 # Prevent infinite loops
for iteration in range(max_iterations):
concat_portfolio = pd.DataFrame(columns=portfolio.columns)
for team in portfolio['Stack'].unique():
rows_to_drop = []
working_portfolio = portfolio.copy()
# Remove players from previous iteration if any
if remove_list:
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])
# Check player exposure
if len(concat_portfolio) == 0:
break
player_exposure = {}
for col in player_columns:
for player in concat_portfolio[col].unique():
if pd.notna(player): # Skip NaN values
player_mask = concat_portfolio[player_columns].apply(
lambda row: player in list(row), axis=1
)
exposure = player_mask.sum() / len(concat_portfolio)
player_exposure[player] = exposure
# Find players with exposure > 35%
high_exposure_players = [player for player, exposure in player_exposure.items() if exposure > 0.35]
# If no high-exposure players, we're done
if not high_exposure_players:
break
# Add high-exposure players to remove list
remove_list.extend(high_exposure_players)
remove_list = list(set(remove_list)) # Remove duplicates
# Check if we have enough lineups and no high-exposure players
if len(concat_portfolio) >= lineup_target and len(high_exposure_players) == 0:
return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)
return concat_portfolio.sort_values(by='Finish_percentile', ascending=True)
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