TrueSkill によるフィッティング

fit_by_trueskill.py

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

# In[1]:


from pathlib import Path


# In[2]:


import pandas as pd
import trueskill
from trueskill import rate_1vs1


# In[3]:


def expose_and_clip(rating, env=None, minimum=0., maximum=50.):
    env = env if env else trueskill.global_env()
    return min(max(minimum, env.expose(rating)), maximum)


# In[4]:


env = trueskill.TrueSkill()


# In[5]:


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


# 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 = []
mu_rows = []
sigma_rows = []


# In[9]:


rating_row = {}
mu_row = {}
sigma_row = {}
for players_name in players_names:
    rating_row[players_name] = expose_and_clip(players[players_name], env=env)
    mu_row[players_name] = players[players_name].mu
    sigma_row[players_name] = players[players_name].sigma
rating_rows.append(pd.Series(rating_row))
mu_rows.append(pd.Series(mu_row))
sigma_rows.append(pd.Series(sigma_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_1vs1(winner, loser, env=env)
        players[winner_name] = new_winner
        players[loser_name] = new_loser
        
    rating_row = {}
    mu_row = {}
    sigma_row = {}
    for players_name in players_names:
        rating_row[players_name] = expose_and_clip(players[players_name], env=env)
        mu_row[players_name] = players[players_name].mu
        sigma_row[players_name] = players[players_name].sigma
    rating_rows.append(pd.Series(rating_row))
    mu_rows.append(pd.Series(mu_row))
    sigma_rows.append(pd.Series(sigma_row))


# In[11]:


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


# In[12]:


mu_df = pd.DataFrame(mu_rows).reset_index()
sigma_df = pd.DataFrame(sigma_rows).reset_index()


# In[13]:


trueskill_rating_csv = Path('./trueskill_rating.csv')
trueskill_mu_csv = Path('./trueskill_mu.csv')
trueskill_sigma_csv = Path('./trueskill_sigma.csv')
rating_df.to_csv(trueskill_rating_csv, index=False)
mu_df.to_csv(trueskill_mu_csv, index=False)
sigma_df.to_csv(trueskill_sigma_csv, index=False)