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source/tools/lobbybots/tests/test_elo.py

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