Elo によるフィッティング

fit_by_elo.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


from pathlib import Path


# In[2]:


import pandas as pd


# In[3]:


class Player:
    def __init__(self, rating=1500, rate_num=0):
        self.rating = rating
        self.rate_num = rate_num
    def win_probability(self, rival):
        return 1. / (10. ** ((rival.rating - self.rating) / 400.) + 1.)
    def win(self, rival):
        return random.random() < self.win_probability(rival)
    @property
    def K(self):
        return 24 if self.rate_num >= 20 else 32


# In[4]:


def rate(winner, loser, drawn=False):
    winner_result = 1. if not drawn else 0.5
    loser_result = 0. if not drawn else 0.5
    new_winner_rating = winner.rating + winner.K * (winner_result - winner.win_probability(loser))
    new_loser_rating = loser.rating + loser.K * (loser_result - loser.win_probability(winner))
    new_winner = Player(new_winner_rating, winner.rate_num + 1)
    new_loser = Player(new_loser_rating, loser.rate_num + 1)
    return new_winner,new_loser,


# In[5]:


players = {
    'Emu': Player(),
    'Parado': Player(),
    'Niko': Player(),
    'Taiga': Player(),
    'Hiiro': Player(),
    'Kiriya': Player(),
    'Kuroto': Player(),
    'Poppy': Player(),
}


# In[6]:


players_names = ['Emu', 'Parado', 'Niko', 'Taiga', 'Hiiro', 'Kiriya', 'Kuroto', 'Poppy']


# In[7]:


simulation_result_csv = Path('./simulation_result.csv')
result_df = pd.read_csv(simulation_result_csv)
result_df


# In[8]:


rating_rows = []


# In[9]:


rating_row = {}
for players_name in players_names:
    rating_row[players_name] = players[players_name].rating
rating_rows.append(pd.Series(rating_row))


# In[10]:


for row_tuple in result_df.iterrows():
    row = row_tuple[1]
    for i in range(4):
        winner_name = row[f'Match {i + 1} Winner']
        loser_name = row[f'Match {i + 1} Loser']
        winner = players[winner_name]
        loser = players[loser_name]
        new_winner,new_loser = rate(winner, loser)
        players[winner_name] = new_winner
        players[loser_name] = new_loser
    rating_row = {}
    for players_name in players_names:
        rating_row[players_name] = players[players_name].rating
    rating_rows.append(pd.Series(rating_row))


# In[11]:


rating_df = pd.DataFrame(rating_rows).reset_index()
rating_df


# In[12]:


elo_rating_csv = Path('./elo_rating.csv')
rating_df.to_csv(elo_rating_csv, index=False)