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_tie_bothbad.py

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+"""
+Regression test for 'tie (bothbad)' handling in optimized_elo (实验 7-7 排行榜).
+
+Chatbot Arena battle data has four outcomes; 'tie (bothbad)' was missing from
+the outcome map, so Series.map produced NaN. NaN then propagated through the
+rating updates and spread to every model that later faced an affected one,
+leaving the whole leaderboard NaN.
+"""
+import numpy as np
+import pandas as pd
+
+from optimized_elo import NumpyEloRatingSystem
+
+
+def test_tie_bothbad_does_not_produce_nan_outcomes():
+    """'tie (bothbad)' maps to a tie instead of NaN."""
+    df = pd.DataFrame({
+        "model_a": ["a", "a"],
+        "model_b": ["b", "b"],
+        "winner": ["model_a", "tie (bothbad)"],
+    })
+    _, _, outcomes = NumpyEloRatingSystem()._prepare_data(df)
+    assert not np.isnan(outcomes).any()
+    assert outcomes[1] == 0.5
+
+
+def test_tie_bothbad_does_not_poison_the_leaderboard():
+    """One 'tie (bothbad)' battle used to NaN every rating, including model c."""
+    df = pd.DataFrame({
+        "model_a": ["a", "a", "b"],
+        "model_b": ["b", "b", "c"],
+        "winner": ["model_a", "tie (bothbad)", "model_a"],
+    })
+    system = NumpyEloRatingSystem()
+    system.process_matches_vectorized(df, show_progress=False)
+    ratings = [rating for _, rating, _, _ in system.get_leaderboard()]
+    assert len(ratings) == 3
+    assert not any(np.isnan(r) for r in ratings)
+
+
+def test_unknown_outcome_falls_back_to_tie():
+    """An unrecognized label degrades to a tie rather than NaN."""
+    df = pd.DataFrame({
+        "model_a": ["a"],
+        "model_b": ["b"],
+        "winner": ["something_new"],
+    })
+    _, _, outcomes = NumpyEloRatingSystem()._prepare_data(df)
+    assert outcomes[0] == 0.5