default to no split.

python-package/xgboost/dask/__init__.py

@@ -1926,7 +1926,7 @@ def __init__(
         self,
         *,
         objective: str = "rank:pairwise",
-        allow_group_split: bool = True,
+        allow_group_split: bool = False,
         coll_cfg: Optional[CollConfig] = None,
         **kwargs: Any,
     ) -> None:

python-package/xgboost/testing/__init__.py

@@ -642,6 +642,9 @@ def non_increasing(L: Sequence[float], tolerance: float = 1e-4) -> bool:
     return all((y - x) < tolerance for x, y in zip(L, L[1:]))
 
 
+def non_decreasing(L: Sequence[float], tolerance: float = 1e-4) -> bool:
+    return all((y - x) >= -tolerance for x, y in zip(L, L[1:]))
+
 def predictor_equal(lhs: xgb.DMatrix, rhs: xgb.DMatrix) -> bool:
     """Assert whether two DMatrices contain the same predictors."""
     lcsr = lhs.get_data()

tests/test_distributed/test_with_dask/test_ranking.py

@@ -61,7 +61,10 @@ def test_dask_ranking(client: Client) -> None:
     qid_test = qid_test.astype(np.uint32)
 
     rank = dxgb.DaskXGBRanker(
-        n_estimators=2500, eval_metric=["ndcg"], early_stopping_rounds=10
+        n_estimators=2500,
+        eval_metric=["ndcg"],
+        early_stopping_rounds=10,
+        allow_group_split=True,
     )
     rank.fit(
         x_train,
@@ -88,4 +91,7 @@ def test_no_group_split(client: Client) -> None:
         eval_qid=[q_tr, q_va],
         verbose=True,
     )
-    print(X_tr.shape, X_tr.columns)
+
+    assert ltr.n_features_in_ == 128
+    assert X_tr.shape[1] == ltr.n_features_in_  # no change
+    assert tm.non_decreasing(ltr.evals_result()["validation_0"]["ndcg@32"])