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dd19cc3444
Reviewers: elexis, Dunedan Fixes: #5914 Differential Revision: https://code.wildfiregames.com/D2630 This was SVN commit r25424.
150 lines
6.8 KiB
Python
150 lines
6.8 KiB
Python
# Copyright (C) 2021 Wildfire Games.
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# This file is part of 0 A.D.
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#
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# 0 A.D. is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 2 of the License, or
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# (at your option) any later version.
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#
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# 0 A.D. is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with 0 A.D. If not, see <http://www.gnu.org/licenses/>.
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"""Tests for the ELO-implementation."""
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from unittest import TestCase
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from hypothesis import assume, example, given
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from hypothesis import strategies as st
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from parameterized import parameterized
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from xpartamupp.elo import (get_rating_adjustment, ANTI_INFLATION, ELO_K_FACTOR_CONSTANT_RATING,
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ELO_SURE_WIN_DIFFERENCE, VOLATILITY_CONSTANT)
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class TestELO(TestCase):
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"""Test behavior of ELO calculation."""
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@parameterized.expand([
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([1000, 1000, 0, 0, 1], 82),
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([1000, 1000, 0, 0, -1], -83),
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([1000, 1000, 0, 0, 0], 0),
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([1200, 1200, 0, 0, 1], 78),
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([1200, 1200, 0, 0, -1], -78),
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([1200, 1200, 0, 0, 0], 0),
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([1200, 1200, 1, 0, 1], 65),
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([1200, 1200, 1, 0, 0], 0),
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([1200, 1200, 1, 0, -1], -65),
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([1200, 1200, 100, 0, 1], 16),
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([1200, 1200, 100, 0, 0], 0),
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([1200, 1200, 100, 0, -1], -16),
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([1200, 1200, 1000, 0, 1], 16),
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([1200, 1200, 1000, 0, 0], 0),
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([1200, 1200, 1000, 0, -1], -16),
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([1200, 1200, 0, 1, 1], 78),
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([1200, 1200, 0, 1, 0], 0),
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([1200, 1200, 0, 1, -1], -78),
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([1200, 1200, 0, 100, 1], 78),
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([1200, 1200, 0, 100, 0], 0),
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([1200, 1200, 0, 100, -1], -78),
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([1200, 1200, 0, 1000, 1], 78),
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([1200, 1200, 0, 1000, 0], 0),
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([1200, 1200, 0, 1000, -1], -78),
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([1400, 1000, 0, 0, 1], 24),
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([1400, 1000, 0, 0, 0], -49),
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([1400, 1000, 0, 0, -1], -122),
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([1000, 1400, 0, 0, 1], 137),
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([1000, 1400, 0, 0, 0], 55),
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([1000, 1400, 0, 0, -1], -28),
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([2200, 2300, 0, 0, 1], 70),
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([2200, 2300, 0, 0, 0], 10),
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([2200, 2300, 0, 0, -1], -50),
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])
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def test_valid_adjustments(self, args, expected_adjustment):
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"""Test correctness of valid rating adjustments."""
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self.assertEqual(get_rating_adjustment(*args), expected_adjustment)
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@given(st.integers(min_value=ELO_K_FACTOR_CONSTANT_RATING),
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st.integers(min_value=-2099, max_value=ELO_SURE_WIN_DIFFERENCE - 1), st.integers(),
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st.integers(),
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st.integers(min_value=-1, max_value=1))
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@example(ELO_K_FACTOR_CONSTANT_RATING + 300, 0, 0, 0, 1)
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def test_constant_rating(self, rating_player1, difference_player2, played_games_player1,
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played_games_player2, result):
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"""Test that points gained are constant above a threshold."""
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volatility = 50.0 * (min(max(0, played_games_player1), VOLATILITY_CONSTANT) /
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VOLATILITY_CONSTANT + 0.25) / 1.25
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rating_adjustment = (difference_player2 + result * ELO_SURE_WIN_DIFFERENCE) / volatility \
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- ANTI_INFLATION
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if result == 1:
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expected_adjustment = max(0.0, rating_adjustment)
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elif result == -1:
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expected_adjustment = min(0.0, rating_adjustment)
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else:
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expected_adjustment = rating_adjustment
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self.assertEqual(get_rating_adjustment(rating_player1, rating_player1 + difference_player2,
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played_games_player1, played_games_player2, result),
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round(expected_adjustment))
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@given(st.data())
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def test_sure_win(self, data):
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"""Test behavior if winning player 1 has >600 points more.
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In this case the winning player shouldn't gain points, as it
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was a "sure win" and the loosing player shouldn't loose
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points.
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"""
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rating_player1 = data.draw(st.integers(min_value=-1599))
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difference_player2 = data.draw(st.integers(min_value=ELO_SURE_WIN_DIFFERENCE))
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assume(rating_player1 - difference_player2 > -2200)
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played_games_player1 = data.draw(st.integers())
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played_games_player2 = data.draw(st.integers())
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self.assertEqual(get_rating_adjustment(rating_player1,
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rating_player1 - difference_player2,
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played_games_player1, played_games_player2, 1),
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0)
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self.assertEqual(get_rating_adjustment(rating_player1 - difference_player2,
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rating_player1, played_games_player2,
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played_games_player1, -1), 0)
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@given(st.integers(min_value=-2199), st.integers(min_value=ELO_SURE_WIN_DIFFERENCE),
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st.integers(),
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st.integers())
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@example(1000, ELO_SURE_WIN_DIFFERENCE, 0, 0)
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def test_sure_loss(self, rating_player1, difference_player2, played_games_player1,
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played_games_player2):
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"""Test behavior if winning player 2 has >600 points more.
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In this case the winning player shouldn't gain points, as it
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was a "sure win" and the loosing player shouldn't loose
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points.
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"""
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self.assertEqual(get_rating_adjustment(rating_player1,
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rating_player1 - difference_player2 * -1,
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played_games_player1, played_games_player2, -1),
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0)
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self.assertEqual(get_rating_adjustment(rating_player1 - difference_player2 * -1,
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rating_player1, played_games_player2,
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played_games_player1, 1), 0)
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@given(st.integers(max_value=-2200), st.integers(),
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st.integers(),
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st.integers(),
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st.one_of(st.just(1), st.just(-1)))
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@example(-2200, 2000, 0, 0, 1)
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@example(2000, -2200, 0, 0, 1)
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def test_minus_2200_bug_workaround(self, rating_player1, rating_player2,
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played_games_player1, played_games_player2, result):
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"""Test workaround for -2200 bug."""
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with self.assertRaises(ValueError):
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get_rating_adjustment(rating_player1, rating_player2, played_games_player1,
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played_games_player2, result)
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with self.assertRaises(ValueError):
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get_rating_adjustment(rating_player2, rating_player1, played_games_player1,
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played_games_player2, result)
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