James McCool
Add debug print statements in hedging_preset.py to output player and lineup data during processing. Adjusted lineup target calculations to use list size directly, enhancing clarity in the lineup generation logic.
4ed60b8
| import pandas as pd | |
| import math | |
| from global_func.small_field_preset import small_field_preset | |
| from global_func.large_field_preset import large_field_preset | |
| def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file: pd.DataFrame): | |
| excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Similarity Score'] | |
| list_size = 3 | |
| check_own_df = projections_file.copy() | |
| check_own_df = check_own_df.sort_values(by='ownership', ascending=False) | |
| top_owned = check_own_df['player_names'].head(list_size).tolist() | |
| concat_portfolio = pd.DataFrame(columns=portfolio.columns) | |
| for players in top_owned: | |
| print(players) | |
| 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] | |
| remove_mask = working_df[player_columns].apply( | |
| lambda row: not any(player in list(row) for player in players), axis=1 | |
| ) | |
| lock_mask = working_df[player_columns].apply( | |
| lambda row: all(player in list(row) for player in players), axis=1 | |
| ) | |
| removed_df = working_df[remove_mask] | |
| print(removed_df.head(10)) | |
| locked_df = working_df[lock_mask] | |
| print(locked_df.head(10)) | |
| removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / list_size), excluded_cols) | |
| locked_lineups = large_field_preset(locked_df, math.ceil(lineup_target / list_size), excluded_cols) | |
| concat_portfolio = pd.concat([concat_portfolio, removed_lineups, locked_lineups]) | |
| return concat_portfolio.head(lineup_target) |