James McCool
Enhance Late Swap optimization with additional player filtering and UI improvements
2d1ce07
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import time | |
| from fuzzywuzzy import process | |
| def optimize_lineup(row): | |
| current_lineup = [] | |
| total_salary = 0 | |
| salary_cap = 50000 | |
| used_players = set() | |
| # Convert row to dictionary with roster positions | |
| roster = {} | |
| for col, player in zip(row.index, row): | |
| if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']: | |
| roster[col] = { | |
| 'name': player, | |
| 'position': map_dict['pos_map'].get(player, '').split('/'), | |
| 'team': map_dict['team_map'].get(player, ''), | |
| 'salary': map_dict['salary_map'].get(player, 0), | |
| 'median': map_dict['proj_map'].get(player, 0), | |
| 'ownership': map_dict['own_map'].get(player, 0) | |
| } | |
| total_salary += roster[col]['salary'] | |
| used_players.add(player) | |
| # Optimize each roster position in random order | |
| roster_positions = list(roster.items()) | |
| random.shuffle(roster_positions) | |
| for roster_pos, current in roster_positions: | |
| # Skip optimization for players from removed teams | |
| if current['team'] in remove_teams_var: | |
| continue | |
| valid_positions = position_rules[roster_pos] | |
| better_options = [] | |
| # Find valid replacements for this roster position | |
| for pos in valid_positions: | |
| if pos in position_groups: | |
| pos_options = [ | |
| p for p in position_groups[pos] | |
| if p['median'] > current['median'] | |
| and (total_salary - current['salary'] + p['salary']) <= salary_cap | |
| and p['player_names'] not in used_players | |
| and any(valid_pos in p['positions'] for valid_pos in valid_positions) | |
| and map_dict['team_map'].get(p['player_names']) not in remove_teams_var # Check team restriction | |
| ] | |
| better_options.extend(pos_options) | |
| if better_options: | |
| # Remove duplicates | |
| better_options = {opt['player_names']: opt for opt in better_options}.values() | |
| # Sort by median projection and take the best one | |
| best_replacement = max(better_options, key=lambda x: x['median']) | |
| # Update the lineup and tracking variables | |
| used_players.remove(current['name']) | |
| used_players.add(best_replacement['player_names']) | |
| total_salary = total_salary - current['salary'] + best_replacement['salary'] | |
| roster[roster_pos] = { | |
| 'name': best_replacement['player_names'], | |
| 'position': map_dict['pos_map'][best_replacement['player_names']].split('/'), | |
| 'team': map_dict['team_map'][best_replacement['player_names']], | |
| 'salary': best_replacement['salary'], | |
| 'median': best_replacement['median'], | |
| 'ownership': best_replacement['ownership'] | |
| } | |
| # Return optimized lineup maintaining original column order | |
| return [roster[pos]['name'] for pos in row.index if pos in roster] |