Rework the MAP metric. (#8931)

- The new implementation is more strict as only binary labels are accepted. The previous implementation converts values greater than 1 to 1.
- Deterministic GPU. (no atomic add).
- Fix top-k handling.
- Precise definition of MAP. (There are other variants on how to handle top-k).
- Refactor GPU ranking tests.

python-package/xgboost/testing/__init__.py

@@ -14,6 +14,7 @@ import zipfile
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
 from io import StringIO
+from pathlib import Path
 from platform import system
 from typing import (
     Any,
@@ -443,7 +444,7 @@ def get_mq2008(
     from sklearn.datasets import load_svmlight_files
 
     src = "https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip"
-    target = dpath + "/MQ2008.zip"
+    target = os.path.join(os.path.expanduser(dpath), "MQ2008.zip")
     if not os.path.exists(target):
         request.urlretrieve(url=src, filename=target)
 
@@ -462,9 +463,9 @@ def get_mq2008(
         qid_valid,
     ) = load_svmlight_files(
         (
-            dpath + "MQ2008/Fold1/train.txt",
-            dpath + "MQ2008/Fold1/test.txt",
-            dpath + "MQ2008/Fold1/vali.txt",
+            Path(dpath) / "MQ2008" / "Fold1" / "train.txt",
+            Path(dpath) / "MQ2008" / "Fold1" / "test.txt",
+            Path(dpath) / "MQ2008" / "Fold1" / "vali.txt",
         ),
         query_id=True,
         zero_based=False,

python-package/xgboost/testing/ranking.py

@@ -48,7 +48,12 @@ def run_ranking_qid_df(impl: ModuleType, tree_method: str) -> None:
     def neg_mse(*args: Any, **kwargs: Any) -> float:
         return -float(mean_squared_error(*args, **kwargs))
 
-    ranker = xgb.XGBRanker(n_estimators=3, eval_metric=neg_mse, tree_method=tree_method)
+    ranker = xgb.XGBRanker(
+        n_estimators=3,
+        eval_metric=neg_mse,
+        tree_method=tree_method,
+        disable_default_eval_metric=True,
+    )
     ranker.fit(df, y, eval_set=[(valid_df, y)])
     score = ranker.score(valid_df, y)
     assert np.isclose(score, ranker.evals_result()["validation_0"]["neg_mse"][-1])