def predict_stage():
past_games, future_games = load_games()
predictor = PlayerTrueSkillPredictor()
predictor.train_games(past_games)
p_stage = predictor.predict_stage(future_games)
teams = sorted(p_stage.keys(),
key=lambda team: p_stage[team][-1],
reverse=True)
print(predictor.base_stage)
print(f' Top3 Top1 Roster')
for team in teams:
p_top3, p_top1 = p_stage[team]
if isinstance(p_top3, bool):
top3 = str(p_top3)
else:
top3 = f'{round(p_top3 * 100)}%'
if isinstance(p_top1, bool):
top1 = str(p_top1)
else:
top1 = f'{round(p_top1 * 100)}%'
roster = ' '.join(predictor.best_rosters[team])
print(f'{team:>4} {top3:>5} {top1:>5} {roster}')
def compare_methods() -> None:
games, _ = load_games()
classes = [SimplePredictor, TrueSkillPredictor, PlayerTrueSkillPredictor]
for class_ in classes:
predictor = class_()
avg_point = predictor.train_games(games) / len(games)
avg_accuracy = np.sum(np.array(predictor.corrects)) / len(games)
print(f'{class_.__name__:>30} {avg_point:8.4f} {avg_accuracy:7.3f}')
def optimize_beta(class_=PlayerTrueSkillPredictor, maxfun=100) -> None:
games, _ = load_games()
def f(x):
predictor = class_(beta=x[0])
return -predictor.train_games(games)
args = fmin(f, [2500.0 / 6.0], maxfun=maxfun)
avg_point = -f(args) / len(games)
print(f'beta = {args[0]:.0f}, avg(point) = {avg_point:.4f}')
def optimize_draw_probability(class_=PlayerTrueSkillPredictor,
maxfun=100) -> None:
games, _ = load_games()
def f(x):
predictor = class_(draw_probability=x[0])
predictor.train_games(games)
return (predictor.expected_draws - predictor.real_draws)**2
args = fmin(f, [0.06], maxfun=maxfun)
print(f'draw_probability = {args[0]:.3f}')
def render_all():
past_games, future_matches = load_games()
predictor = PlayerTrueSkillPredictor()
match_cards = render_match_cards(predictor, past_games, future_matches)
predictor.save_ratings_history()
render_index(predictor, future_matches)
render_matches(match_cards)
render_teams(predictor, match_cards)
render_about(predictor)
def predict_stage():
past_games, future_matches = load_games()
predictor = PlayerTrueSkillPredictor()
predictor.train_games(past_games)
p_stage = predictor.predict_stage(future_matches)
# p_season = predictor.predict_season(future_matches)
teams = sorted(p_stage.keys(),
key=lambda team: p_stage[team][-1],
reverse=True)
print(predictor.base_stage)
print(f' Title Top1 Roster')
for team in teams:
p_title, p_top1 = p_stage[team]
# p_season_top6, p_season_top1 = p_season[team]
if isinstance(p_title, bool):
title = str(p_title)
else:
title = f'{round(p_title * 100)}%'
if isinstance(p_top1, bool):
top1 = str(p_top1)
else:
top1 = f'{round(p_top1 * 100)}%'
# if isinstance(p_season_top6, bool):
# season_top6 = str(p_season_top6)
# else:
# season_top6 = f'{round(p_season_top6 * 100)}%'
# if isinstance(p_season_top1, bool):
# season_top1 = str(p_season_top1)
# else:
# season_top1 = f'{round(p_season_top1 * 100)}%'
roster = ' '.join(predictor.best_rosters[team])
print(f'{team:>4} {title:>5} {top1:>5} {roster}')
def save_ratings():
past_games, future_games = load_games()
predictor = PlayerTrueSkillPredictor()
predictor.train_games(past_games)
predictor.save_ratings_history()