Update elo.py

elo.py

@@ -1,37 +1,57 @@
 import pandas as pd
 from datasets import Dataset
 
-def calculate_elo(old_rating, opponent_rating, score, k_factor=32):
+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 (default is 32).
+    :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, k_factor=32):
+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}
-            ratings_df = pd.concat([ratings_df,pd.DataFrame([new_player])],ignore_index=True)
+            new_player = {'bot_name': player, 'elo_rating': 1200, 'games_played': 0}
+            ratings_df = pd.concat([ratings_df, pd.DataFrame([new_player])], ignore_index=True)
 
-    # Extract old ratings
+    # 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, k_factor)
-    loser_new_rating = calculate_elo(loser_old_rating, winner_old_rating, 0, k_factor)
+    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
@@ -40,4 +60,4 @@ def update_elo_ratings(ratings_dataset, winner, loser, k_factor=32):
     # 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