woggioni
5a73601ddf
Move the game-independent machinery (lobby, live-game store, websocket, deadline scheduler, match history, leaderboards) into a new tavolo-platform distribution behind a GameEngine contract, the scopone scientifico rules plus a platform adapter into tavolo-scopone, and keep only the composition root in tavolo-app. The three distributions share the tavolo namespace (PEP 420, kaya-style monorepo). Match history becomes fully generic: Match carries the engine's result JSON and MatchPlayer points/details instead of scopone-shaped team columns (migration 3 backfills existing rows). Lobby creation takes an opaque per-game options object and websocket actions dispatch to the session's engine. Tests: platform suite runs against a DummyEngine toy game, scopone keeps the rules tests plus new adapter tests, server/tests covers the wired stack end to end (194 tests, was 143).
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
"""Unit tests for the chess-style Elo math in :mod:`tavolo.platform.elo`."""
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from __future__ import annotations
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import unittest
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from tavolo.platform.elo import (
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INITIAL_RATING,
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K_FACTOR,
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expected_score,
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match_delta,
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team_rating,
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)
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class ExpectedScoreTest(unittest.TestCase):
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def test_equal_ratings_give_even_odds(self) -> None:
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self.assertAlmostEqual(0.5, expected_score(1500, 1500))
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def test_higher_rating_is_favoured(self) -> None:
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self.assertGreater(expected_score(1700, 1500), 0.5)
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self.assertLess(expected_score(1500, 1700), 0.5)
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def test_scores_sum_to_one(self) -> None:
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self.assertAlmostEqual(
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1.0, expected_score(1600, 1400) + expected_score(1400, 1600)
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)
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def test_four_hundred_points_is_ten_to_one(self) -> None:
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self.assertAlmostEqual(10 / 11, expected_score(1900, 1500))
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class TeamRatingTest(unittest.TestCase):
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def test_mean_of_members(self) -> None:
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self.assertEqual(1600, team_rating([1500, 1700]))
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def test_empty_team_rejected(self) -> None:
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with self.assertRaises(ValueError):
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team_rating([])
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class MatchDeltaTest(unittest.TestCase):
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def test_equal_teams_exchange_half_k(self) -> None:
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delta = match_delta([1500, 1500], [1500, 1500], winner_team=0)
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self.assertEqual(K_FACTOR // 2, delta)
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def test_favourite_gains_less_than_underdog(self) -> None:
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favourite = match_delta([1700, 1700], [1500, 1500], winner_team=0)
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underdog = match_delta([1500, 1500], [1700, 1700], winner_team=0)
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self.assertGreater(underdog, favourite)
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self.assertGreater(favourite, 0)
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def test_losing_side_loses_the_winners_gain(self) -> None:
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# Zero-sum: the losers' delta is the negation of the winners'.
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win = match_delta([1600, 1500], [1400, 1500], winner_team=0)
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loss = match_delta([1600, 1500], [1400, 1500], winner_team=1)
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self.assertEqual(-win, -abs(win)) # winner gains
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# Losing the same pairing costs K * E, winning gains K * (1 - E);
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# both are computed from the same expectation, so loss = win - K.
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self.assertEqual(win - K_FACTOR, loss)
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def test_team_average_decides_not_individual_ratings(self) -> None:
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# [1700, 1300] averages 1500, same as [1500, 1500].
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mixed = match_delta([1700, 1300], [1500, 1500], winner_team=0)
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even = match_delta([1500, 1500], [1500, 1500], winner_team=0)
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self.assertEqual(even, mixed)
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def test_initial_rating_constant(self) -> None:
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self.assertEqual(1500, INITIAL_RATING)
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self.assertEqual(32, K_FACTOR)
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if __name__ == "__main__":
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unittest.main()
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