Use dataclass.

demo/guide-python/learning_to_rank.py

@@ -121,31 +121,26 @@ def ranking_demo(args: argparse.Namespace) -> None:
 def click_data_demo(args: argparse.Namespace) -> None:
     """Demonstration for learning to rank with click data."""
     data = load_mlsr_10k(args.data, args.cache)
-    folds = simulate_clicks(data)
+    train, test = simulate_clicks(data)
 
-    train = [pack[0] for pack in folds]
-    test = [pack[1] for pack in folds]
-
-    X_train, y_train, qid_train, scores_train, clicks_train, position_train = train
-    assert X_train.shape[0] == clicks_train.size
-    X_test, y_test, qid_test, scores_test, clicks_test, position_test = test
-    assert X_test.shape[0] == clicks_test.size
-    assert scores_test.dtype == np.float32
-    assert clicks_test.dtype == np.int32
+    assert train.X.shape[0] == train.click.size
+    assert test.X.shape[0] == test.click.size
+    assert test.score.dtype == np.float32
+    assert test.click.dtype == np.int32
 
     X_train, clicks_train, y_train, qid_train = sort_ltr_samples(
-        X_train,
-        y_train,
-        qid_train,
-        clicks_train,
-        position_train,
+        train.X,
+        train.y,
+        train.qid,
+        train.click,
+        train.pos,
     )
     X_test, clicks_test, y_test, qid_test = sort_ltr_samples(
-        X_test,
-        y_test,
-        qid_test,
-        clicks_test,
-        position_test,
+        test.X,
+        test.y,
+        test.qid,
+        test.click,
+        test.pos,
     )
 
     class ShowPosition(xgb.callback.TrainingCallback):

python-package/xgboost/testing/data.py

@@ -3,6 +3,7 @@
 import os
 import zipfile
 from collections import namedtuple
+from dataclasses import dataclass
 from typing import Any, Generator, List, NamedTuple, Tuple, Union
 from urllib import request
 
@@ -345,10 +346,22 @@ def get_mq2008(
     )
 
 
-ClickFold = namedtuple("ClickFold", ("X", "y", "q", "s", "c", "p"))
 RelData = Tuple[sparse.csr_matrix, npt.NDArray[np.int32], npt.NDArray[np.int32]]
 
 
+@dataclass
+class ClickFold:
+    """A structure containing information about generated user-click data.
+
+    """
+    X: sparse.csr_matrix
+    y: npt.NDArray[np.int32]
+    qid: npt.NDArray[np.int32]
+    score: npt.NDArray[np.float32]
+    click: npt.NDArray[np.int32]
+    pos: npt.NDArray[np.int64]
+
+
 class RelDataCV(NamedTuple):
     """Simple data struct for train-test split."""
 
@@ -499,7 +512,8 @@ def simulate_one_fold(
     return ClickFold(X_fold, y_fold, qid_fold, scores_fold, clicks, position)
 
 
-def simulate_clicks(cv_data: RelDataCV) -> ClickFold:  # pylint: disable=too-many-locals
+# pylint: disable=too-many-locals
+def simulate_clicks(cv_data: RelDataCV) -> Tuple[ClickFold, ClickFold]:
     """Simulate click data using position biased model (PBM)."""
     X, y, qid = list(zip(cv_data.train, cv_data.test))
 
@@ -518,21 +532,23 @@ def simulate_clicks(cv_data: RelDataCV) -> ClickFold:  # pylint: disable=too-man
 
     X_lst, y_lst, q_lst, s_lst, c_lst, p_lst = [], [], [], [], [], []
     for i in range(indptr.size - 1):
-        x_, y_, q_, s_, c_, p_ = simulate_one_fold((X[i], y[i], qid[i]), scores[i])
-        X_lst.append(x_)
-        y_lst.append(y_)
-        q_lst.append(q_)
-        s_lst.append(s_)
-        c_lst.append(c_)
-        p_lst.append(p_)
+        fold = simulate_one_fold((X[i], y[i], qid[i]), scores[i])
+        X_lst.append(fold.X)
+        y_lst.append(fold.y)
+        q_lst.append(fold.qid)
+        s_lst.append(fold.score)
+        c_lst.append(fold.click)
+        p_lst.append(fold.pos)
 
     scores_check_1 = [s_lst[i] for i in range(indptr.size - 1)]
     for i in range(2):
         assert (scores_check_1[i] == scores[i]).all()
 
-    data = ClickFold(X_lst, y_lst, q_lst, s_lst, c_lst, p_lst)
-
-    return data
+    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)
+    ]
+    return train, test
 
 
 def sort_ltr_samples(