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James McCool
Refactor import statements across multiple files to replace 'fuzzywuzzy' with 'rapidfuzz' for improved performance and consistency in string matching functionality. Additionally, clean up unused imports in app.py and related global functions to enhance code clarity and maintainability.
d9db89f
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import time | |
| from rapidfuzz 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] |