Check support status for categorical features. (#9946)

tests/python/test_data_iterator.py

@@ -1,3 +1,5 @@
+import os
+import tempfile
 import weakref
 from typing import Any, Callable, Dict, List
 
@@ -195,3 +197,39 @@ def test_data_cache() -> None:
     assert called == 1
 
     xgb.data._proxy_transform = transform
+
+
+def test_cat_check() -> None:
+    n_batches = 3
+    n_features = 2
+    n_samples_per_batch = 16
+
+    batches = []
+
+    for i in range(n_batches):
+        X, y = tm.make_categorical(
+            n_samples=n_samples_per_batch,
+            n_features=n_features,
+            n_categories=3,
+            onehot=False,
+        )
+        batches.append((X, y))
+
+    X, y = list(zip(*batches))
+    it = tm.IteratorForTest(X, y, None, cache=None)
+    Xy: xgb.DMatrix = xgb.QuantileDMatrix(it, enable_categorical=True)
+
+    with pytest.raises(ValueError, match="categorical features"):
+        xgb.train({"tree_method": "exact"}, Xy)
+
+    Xy = xgb.DMatrix(X[0], y[0], enable_categorical=True)
+    with pytest.raises(ValueError, match="categorical features"):
+        xgb.train({"tree_method": "exact"}, Xy)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cache_path = os.path.join(tmpdir, "cache")
+
+        it = tm.IteratorForTest(X, y, None, cache=cache_path)
+        Xy = xgb.DMatrix(it, enable_categorical=True)
+        with pytest.raises(ValueError, match="categorical features"):
+            xgb.train({"booster": "gblinear"}, Xy)