ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter7/elo-leaderboard/tests/test_elo.py

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+"""
+Unit tests for Elo rating system
+"""
+import math
+
+import pytest
+
+from _bootstrap import bootstrap_experiment_root
+
+bootstrap_experiment_root()
+
+from elo_rating import EloRatingSystem
+
+
+def test_initial_rating():
+    """Test that models start with initial rating."""
+    elo = EloRatingSystem(initial_rating=1000.0)
+    assert elo.get_rating("model_a") == 1000.0
+    assert elo.get_rating("model_b") == 1000.0
+
+
+def test_expected_score():
+    """Test expected score calculation."""
+    elo = EloRatingSystem()
+    
+    # Equal ratings should give 50% probability
+    assert elo.expected_score(1000, 1000) == 0.5
+    
+    # Higher rated player should have > 50% probability
+    assert elo.expected_score(1200, 1000) > 0.5
+    assert elo.expected_score(1000, 1200) < 0.5
+    
+    # 400 point difference should give ~91% probability
+    prob = elo.expected_score(1400, 1000)
+    assert 0.90 < prob < 0.92
+
+
+def test_rating_update_win():
+    """Test rating update when model_a wins."""
+    elo = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    
+    new_a, new_b = elo.update_ratings("model_a", "model_b", "model_a")
+    
+    # Winner should gain rating, loser should lose rating
+    assert new_a > 1000.0
+    assert new_b < 1000.0
+    
+    # Total rating should be conserved (zero-sum)
+    assert abs((new_a + new_b) - 2000.0) < 0.01
+
+
+def test_rating_update_tie():
+    """Test rating update for a tie."""
+    elo = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    
+    new_a, new_b = elo.update_ratings("model_a", "model_b", "tie")
+    
+    # With equal ratings, tie should not change ratings much
+    assert abs(new_a - 1000.0) < 0.01
+    assert abs(new_b - 1000.0) < 0.01
+
+
+def test_upset_gives_larger_change():
+    """Test that unexpected results cause larger rating changes."""
+    elo = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    
+    # Give model_a higher rating
+    elo.ratings["model_a"] = 1200.0
+    elo.ratings["model_b"] = 1000.0
+    
+    # If weaker model wins (upset), changes should be larger
+    new_a_upset, new_b_upset = elo.update_ratings("model_a", "model_b", "model_b")
+    
+    # Reset
+    elo.ratings["model_a"] = 1200.0
+    elo.ratings["model_b"] = 1000.0
+    
+    # If stronger model wins (expected), changes should be smaller
+    new_a_expected, new_b_expected = elo.update_ratings("model_a", "model_b", "model_a")
+    
+    # Upset should cause larger change
+    change_upset = abs(new_a_upset - 1200.0)
+    change_expected = abs(new_a_expected - 1200.0)
+    
+    assert change_upset > change_expected
+
+
+def test_leaderboard_sorting():
+    """Test that leaderboard is sorted by rating."""
+    elo = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    
+    # Create some matches to differentiate ratings
+    elo.update_ratings("model_a", "model_b", "model_a")
+    elo.update_ratings("model_a", "model_c", "model_a")
+    elo.update_ratings("model_b", "model_c", "model_b")
+    
+    leaderboard = elo.get_leaderboard()
+    
+    # Check descending order
+    for i in range(len(leaderboard) - 1):
+        assert leaderboard[i][1] >= leaderboard[i+1][1]
+    
+    # model_a should be first (won all matches)
+    assert leaderboard[0][0] == "model_a"
+
+
+def test_win_probability_symmetry():
+    """Test that win probabilities sum to 1."""
+    elo = EloRatingSystem()
+    elo.ratings["model_a"] = 1200.0
+    elo.ratings["model_b"] = 1000.0
+    
+    prob_a = elo.calculate_win_probability("model_a", "model_b")
+    prob_b = elo.calculate_win_probability("model_b", "model_a")
+    
+    # Should sum to 1
+    assert abs(prob_a + prob_b - 1.0) < 0.001
+
+
+def test_match_counting():
+    """Test that match and win counts are tracked correctly."""
+    elo = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    
+    elo.update_ratings("model_a", "model_b", "model_a")  # model_a wins
+    elo.update_ratings("model_a", "model_c", "model_b")  # model_a loses (2nd slot wins)
+    elo.update_ratings("model_a", "model_b", "tie")      # tie -> 0.5 each
+    
+    # model_a played 3 matches
+    assert elo.match_counts["model_a"] == 3
+    
+    # model_a won 1 match and tied 1 (1.5 total)
+    assert elo.win_counts["model_a"] == 1.5
+    
+    # model_b played 2 matches
+    assert elo.match_counts["model_b"] == 2
+
+
+def test_copy():
+    """Test that copy creates independent instance."""
+    elo1 = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    elo1.update_ratings("model_a", "model_b", "model_a")
+    
+    elo2 = elo1.copy()
+    
+    # Modify elo2
+    elo2.update_ratings("model_a", "model_b", "model_b")
+    
+    # elo1 should be unchanged
+    assert elo1.ratings["model_a"] != elo2.ratings["model_a"]
+
+
+def test_reset():
+    """Test that reset clears all data."""
+    elo = EloRatingSystem(initial_rating=1000.0, k_factor=32.0)
+    
+    elo.update_ratings("model_a", "model_b", "model_a")
+    elo.update_ratings("model_a", "model_c", "model_a")
+    
+    assert len(elo.ratings) > 0
+    
+    elo.reset()
+    
+    assert len(elo.ratings) == 0
+    assert len(elo.match_counts) == 0
+    assert len(elo.win_counts) == 0
+
+
+if __name__ == "__main__":
+    # Run tests
+    pytest.main([__file__, "-v"])