Update elo.py

elo.py

@@ -1,20 +1,3 @@
-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)
@@ -50,8 +33,7 @@ def update_elo_ratings(ratings_dataset, winner, loser):
     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)
+    winner_new_rating, loser_new_rating = elo(winner_old_rating, loser_old_rating, k_factor_winner=winner_k_factor, k_factor_loser=loser_k_factor)
 
     # Update the DataFrame
     ratings_df.loc[ratings_df['bot_name'] == winner, 'elo_rating'] = winner_new_rating
@@ -60,4 +42,4 @@ def update_elo_ratings(ratings_dataset, winner, loser):
     # Convert the DataFrame back to a Hugging Face dataset
     updated_ratings_dataset = Dataset.from_pandas(ratings_df)
 
-    return updated_ratings_dataset
+    return updated_ratings_dataset