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| import pandas as pd | |
| from datasets import Dataset | |
| def calculate_elo(old_rating, opponent_rating, score, k_factor): | |
| """ | |
| Calculate the new ELO rating for a player. | |
| :param old_rating: The current ELO rating of the player. | |
| :param opponent_rating: The ELO rating of the opponent. | |
| :param score: The score of the game (1 for win, 0.5 for draw, 0 for loss). | |
| :param k_factor: The K-factor used in ELO rating. | |
| :return: The new ELO rating. | |
| """ | |
| expected_score = 1 / (1 + 10 ** ((opponent_rating - old_rating) / 400)) | |
| new_rating = old_rating + k_factor * (score - expected_score) | |
| return new_rating | |
| def update_elo_ratings(ratings_dataset, winner, loser): | |
| # Convert the Hugging Face dataset to a pandas DataFrame | |
| ratings_df = pd.DataFrame(ratings_dataset) | |
| # Check and add new players if they don't exist in the dataset | |
| for player in [winner, loser]: | |
| if player not in ratings_df['bot_name'].values: | |
| new_player = {'bot_name': player, 'elo_rating': 1200, 'games_played': 0} | |
| ratings_df = pd.concat([ratings_df, pd.DataFrame([new_player])], ignore_index=True) | |
| # Function to determine the K-factor based on games played | |
| def determine_k_factor(games_played): | |
| # Define K-factor based on number of games played. Adjust these thresholds as needed. | |
| if games_played < 30: | |
| return 40 | |
| elif games_played < 100: | |
| return 20 | |
| else: | |
| return 10 | |
| # Update games played | |
| ratings_df.loc[ratings_df['bot_name'] == winner, 'games_played'] += 1 | |
| ratings_df.loc[ratings_df['bot_name'] == loser, 'games_played'] += 1 | |
| # Extract old ratings and games played | |
| winner_old_rating = ratings_df.loc[ratings_df['bot_name'] == winner, 'elo_rating'].iloc[0] | |
| loser_old_rating = ratings_df.loc[ratings_df['bot_name'] == loser, 'elo_rating'].iloc[0] | |
| winner_games_played = ratings_df.loc[ratings_df['bot_name'] == winner, 'games_played'].iloc[0] | |
| loser_games_played = ratings_df.loc[ratings_df['bot_name'] == loser, 'games_played'].iloc[0] | |
| # Determine K-factors | |
| winner_k_factor = determine_k_factor(winner_games_played) | |
| loser_k_factor = determine_k_factor(loser_games_played) | |
| # Calculate new ratings | |
| winner_new_rating = calculate_elo(winner_old_rating, loser_old_rating, 1, winner_k_factor) | |
| loser_new_rating = calculate_elo(loser_old_rating, winner_old_rating, 0, loser_k_factor) | |
| # Update the DataFrame | |
| ratings_df.loc[ratings_df['bot_name'] == winner, 'elo_rating'] = winner_new_rating | |
| ratings_df.loc[ratings_df['bot_name'] == loser, 'elo_rating'] = loser_new_rating | |
| # Convert the DataFrame back to a Hugging Face dataset | |
| updated_ratings_dataset = Dataset.from_pandas(ratings_df) | |
| return updated_ratings_dataset | |