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import pandas as pd
from datasets import Dataset
def calculate_elo(old_rating, opponent_rating, score, k_factor=32):
"""
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 (default is 32).
: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, k_factor=32):
"""
Update ELO ratings for two players in a Hugging Face dataset.
:param ratings_dataset: A Hugging Face dataset of current ELO ratings.
:param winner: The name of the winning player.
:param loser: The name of the losing player.
:param k_factor: The K-factor used in ELO rating (default is 32).
:return: Updated ELO ratings as a Hugging Face dataset.
"""
# Convert the Hugging Face dataset to a pandas DataFrame for easier manipulation
ratings_df = pd.DataFrame(ratings_dataset)
# Extract old ratings
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]
# Calculate new ratings
winner_new_rating = calculate_elo(winner_old_rating, loser_old_rating, 1, k_factor)
loser_new_rating = calculate_elo(loser_old_rating, winner_old_rating, 0, 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
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