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| import numpy as np | |
| import pytest | |
| from ding.league import get_elo, get_elo_array, LeagueMetricEnv | |
| def test_elo_calculator(): | |
| game_count = np.array([[0, 1, 2], [1, 0, 0], [2, 0, 0]]) | |
| rating = np.array([1613, 1573, 1601]) | |
| result = np.array([[0, -1, -1 + 1], [1, 0, 0], [1 + (-1), 0, 0]]) | |
| new_rating0, new_rating1 = get_elo(rating[0], rating[1], result[0][1]) | |
| assert new_rating0 == 1595 | |
| assert new_rating1 == 1591 | |
| old_rating = np.copy(rating) | |
| new_rating = get_elo_array(rating, result, game_count) | |
| assert (rating == old_rating).all() # no inplace modification | |
| assert new_rating.dtype == np.int64 | |
| assert new_rating[0] == 1578 | |
| assert new_rating[1] == 1591 | |
| assert new_rating[2] == 1586 | |
| def test_league_metric(): | |
| sigma = 25 / 3 | |
| env = LeagueMetricEnv(mu=0, sigma=sigma, beta=sigma / 2, tau=0.0, draw_probability=0.02, elo_init=1000) | |
| r1 = env.create_rating(elo_init=1613) | |
| r2 = env.create_rating(elo_init=1573) | |
| assert r1.mu == 0 | |
| assert r2.mu == 0 | |
| assert r2.sigma == sigma | |
| assert r2.sigma == sigma | |
| assert r1.elo == 1613 | |
| assert r2.elo == 1573 | |
| # r1 draw r2 | |
| r1, r2 = env.rate_1vs1(r1, r2, drawn=True) | |
| assert r1.mu == r2.mu | |
| assert r1.elo == 1611 | |
| assert r2.elo == 1575 | |
| # r1 win r2 | |
| new_r1, new_r2 = env.rate_1vs1(r1, r2) | |
| assert new_r1.mu > r1.mu | |
| assert new_r2.mu < r2.mu | |
| assert new_r1.mu + new_r2.mu == 0 | |
| assert pytest.approx(new_r1.mu, abs=1e-4) == 3.230 | |
| assert pytest.approx(new_r2.mu, abs=1e-4) == -3.230 | |
| assert new_r1.elo == 1625 | |
| assert new_r2.elo == 1561 | |
| # multi result | |
| new_r1, new_r2 = env.rate_1vs1(r1, r2, result=['wins', 'wins', 'losses']) | |
| assert new_r1.elo > 1611 | |
| # 1vsConstant | |
| new_r1 = env.rate_1vsC(r1, env.create_rating(elo_init=1800), result=['losses', 'losses']) | |
| assert new_r1.elo < 1611 | |
| print('final rating is: ', new_r1) | |