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_bt_tie_pivot.py

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+"""Ties must contribute to Bradley-Terry weights (not be zeroed by pivot+T)."""
+import pandas as pd
+
+from bradley_terry import compute_mle_elo
+
+
+def test_all_ties_rates_models_instead_of_sample_weight_error():
+    df = pd.DataFrame(
+        [
+            {"model_a": "A", "model_b": "B", "winner": "tie"},
+            {"model_a": "A", "model_b": "C", "winner": "tie (bothbad)"},
+            {"model_a": "B", "model_b": "C", "winner": "tie"},
+        ]
+    )
+    ratings = compute_mle_elo(df)
+    assert set(ratings.index) == {"A", "B", "C"}
+    # Pure ties -> equal latent skills under BT.
+    assert abs(float(ratings["A"]) - float(ratings["B"])) < 1e-6
+    assert abs(float(ratings["A"]) - float(ratings["C"])) < 1e-6
+
+
+def test_ties_change_ratings_versus_wins_only():
+    wins_only = pd.DataFrame(
+        [
+            {"model_a": "A", "model_b": "B", "winner": "model_a"},
+            {"model_a": "B", "model_b": "C", "winner": "model_a"},
+        ]
+    )
+    with_ties = pd.concat(
+        [
+            wins_only,
+            pd.DataFrame(
+                [{"model_a": "A", "model_b": "C", "winner": "tie"}] * 8
+            ),
+        ],
+        ignore_index=True,
+    )
+    r1 = compute_mle_elo(wins_only)
+    r2 = compute_mle_elo(with_ties)
+    # Extra A–C ties pull A and C together relative to the wins-only fit.
+    assert abs(float(r2["A"]) - float(r2["C"])) < abs(float(r1["A"]) - float(r1["C"]))