Fix return type.

python-package/xgboost/testing/__init__.py

@@ -464,6 +464,7 @@ def make_position_biased_ltr(
         (X_train, y_train, qid_train), (X_test, y_test, qid_test), max_rel=max_rel
     )
     clicks = simulate_clicks(data)
+    assert isinstance(clicks, ClickFold)
     return clicks
 
 

python-package/xgboost/testing/data.py

@@ -2,9 +2,8 @@
 """Utilities for data generation."""
 import os
 import zipfile
-from collections import namedtuple
 from dataclasses import dataclass
-from typing import Any, Generator, List, NamedTuple, Tuple, Union
+from typing import Any, Generator, List, NamedTuple, Optional, Tuple, Union
 from urllib import request
 
 import numpy as np
@@ -351,9 +350,8 @@ def get_mq2008(
 
 @dataclass
 class ClickFold:
-    """A structure containing information about generated user-click data.
+    """A structure containing information about generated user-click data."""
 
-    """
     X: sparse.csr_matrix
     y: npt.NDArray[np.int32]
     qid: npt.NDArray[np.int32]
@@ -513,7 +511,7 @@ def simulate_one_fold(
 
 
 # pylint: disable=too-many-locals
-def simulate_clicks(cv_data: RelDataCV) -> Tuple[ClickFold, ClickFold]:
+def simulate_clicks(cv_data: RelDataCV) -> Tuple[ClickFold, Optional[ClickFold]]:
     """Simulate click data using position biased model (PBM)."""
     X, y, qid = list(zip(cv_data.train, cv_data.test))
 
@@ -544,10 +542,14 @@ def simulate_clicks(cv_data: RelDataCV) -> Tuple[ClickFold, ClickFold]:
     for i in range(2):
         assert (scores_check_1[i] == scores[i]).all()
 
-    train, test = [
-        ClickFold(X_lst[i], y_lst[i], q_lst[i], s_lst[i], c_lst[i], p_lst[i])
-        for i in range(2)
-    ]
+    if len(X_lst) == 1:
+        train = ClickFold(X_lst[0], y_lst[0], q_lst[0], s_lst[0], c_lst[0], p_lst[0])
+        test = None
+    else:
+        train, test = (
+            ClickFold(X_lst[i], y_lst[i], q_lst[i], s_lst[i], c_lst[i], p_lst[i])
+            for i in range(len(X_lst))
+        )
     return train, test
 
 

tests/python/test_ranking.py

@@ -114,10 +114,10 @@ def test_unbiased() -> None:
     Xe = csr_matrix(Xe)
     data = RelDataCV((X, y, q), (Xe, ye, qe), max_rel=5)
 
-    sim = simulate_clicks(data)
-    train = [pack[0] for pack in sim]
-    x, y, q, s, c, p = train
-    x, c, y, q = sort_ltr_samples(x, y, q, c, p)
+    train, _ = simulate_clicks(data)
+    x, c, y, q = sort_ltr_samples(
+        train.X, train.y, train.qid, train.click, train.pos
+    )
     df: Optional[pd.DataFrame] = None
 
     class Position(xgboost.callback.TrainingCallback):