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import random
import pandas as pd
import os
class Model:
"""
Class containing the info of a model.
:param name: Name of the model
:param elo: Elo rating of the model
:param games_played: Number of games played by the model (useful if we implement sigma uncertainty)
"""
def __init__(self, name, elo):
self.name = name
self.elo = elo
self.games_played = 0
class Matchmaking:
"""
Class managing the matchmaking between the models.
:param models: List of models
:param queue: Temporary list of models used for the matching process
:param k: Dev coefficient
:param max_diff: Maximum difference considered between two models' elo
:param matches: Dictionary containing the match history (to later upload as CSV)
"""
def __init__(self):
self.models = []
self.queue = []
self.start_elo = 1200
self.k = 20
self.max_diff = 500
self.matches = pd.DataFrame()
def read_history(self):
""" Read the match history from the CSV files, concat the Dataframes and sort them by datetime. """
path = "match_history"
files = os.listdir(path)
for file in files:
self.matches = pd.concat([self.matches, pd.read_csv(os.path.join(path, file))], ignore_index=True)
self.matches["datetime"] = pd.to_datetime(self.matches["datetime"], format="%Y-%m-%d %H:%M:%S.%f", errors="coerce")
self.matches = self.matches.dropna()
self.matches = self.matches.sort_values("datetime")
self.matches.reset_index(drop=True, inplace=True)
model_names = self.matches["model1"].unique()
self.models = [Model(name, self.start_elo) for name in model_names]
def compute_elo(self):
""" Compute the elo for each model after each match. """
for i, row in self.matches.iterrows():
model1 = self.get_model(row["model1"])
model2 = self.get_model(row["model2"])
result = row["result"]
delta = model1.elo - model2.elo
win_probability = 1 / (1 + 10 ** (-delta / 500))
model1.elo += self.k * (result - win_probability)
model2.elo -= self.k * (result - win_probability)
model1.games_played += 1
model2.games_played += 1
def save_elo_data(self):
""" Save the match history as a CSV file to the hub. """
df = pd.DataFrame(columns=['name', 'elo'])
for model in self.models:
df = pd.concat([df, pd.DataFrame([[model.name, model.elo]], columns=['name', 'elo'])])
df.to_csv('elo.csv', index=False)
def get_model(self, name):
""" Return the Model with the given name. """
for model in self.models:
if model.name == name:
return model
return None
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