Test.

python-package/xgboost/testing/ranking.py

@@ -118,3 +118,30 @@ def run_normalization(device: str) -> None:
     ltr.fit(X, y, qid=qid, eval_set=[(X, y)], eval_qid=[qid])
     e1 = ltr.evals_result()
     assert e1["validation_0"]["ndcg@32"][-1] > e0["validation_0"]["ndcg@32"][-1]
+
+
+def run_score_normalization(device: str, objective: str) -> None:
+    print()
+    if objective == "rank:map":
+        # Binary relevance
+        X, y, qid, _ = tm.make_ltr(4096, 4, 64, max_rel=1)
+    else:
+        X, y, qid, _ = tm.make_ltr(4096, 4, 64, 3)
+    ltr = xgb.XGBRanker(objective=objective, n_estimators=4, device=device)
+    ltr.fit(X, y, qid=qid, eval_set=[(X, y)], eval_qid=[qid])
+    e0 = ltr.evals_result()
+
+    ltr = xgb.XGBRanker(
+        objective="rank:pairwise",
+        n_estimators=4,
+        device=device,
+        lambdarank_score_normalization=False,
+    )
+    ltr.fit(X, y, qid=qid, eval_set=[(X, y)], eval_qid=[qid])
+    e1 = ltr.evals_result()
+
+    m0, m1 = (
+        list(e0["validation_0"].values())[-1][-1],
+        list(e1["validation_0"].values())[-1][-1],
+    )
+    assert m0 != m1

tests/python-gpu/test_gpu_ranking.py

@@ -6,7 +6,7 @@
 
 import xgboost
 from xgboost import testing as tm
-from xgboost.testing.ranking import run_normalization
+from xgboost.testing.ranking import run_normalization, run_score_normalization
 
 pytestmark = tm.timeout(30)
 
@@ -131,3 +131,8 @@ def test_with_mq2008(objective, metric) -> None:
 
 def test_normalization() -> None:
     run_normalization("cuda")
+
+
+@pytest.mark.parametrize("objective", ["rank:pairwise", "rank:ndcg", "rank:map"])
+def test_score_normalization(objective: str) -> None:
+    run_score_normalization("cuda", objective)

tests/python/test_ranking.py

@@ -13,7 +13,7 @@
 from xgboost import testing as tm
 from xgboost.testing.data import RelDataCV, simulate_clicks, sort_ltr_samples
 from xgboost.testing.params import lambdarank_parameter_strategy
-from xgboost.testing.ranking import run_normalization
+from xgboost.testing.ranking import run_normalization, run_score_normalization
 
 
 def test_ndcg_custom_gain():
@@ -213,6 +213,11 @@ def test_normalization() -> None:
     run_normalization("cpu")
 
 
+@pytest.mark.parametrize("objective", ["rank:pairwise", "rank:ndcg", "rank:map"])
+def test_score_normalization(objective: str) -> None:
+    run_score_normalization("cpu", objective)
+
+
 class TestRanking:
     @classmethod
     def setup_class(cls):