Mixin.

python-package/xgboost/core.py

@@ -1451,7 +1451,18 @@ def _ref_data_from_csr(self, csr: scipy.sparse.csr_matrix) -> None:
         )
 
 
-class QuantileDMatrix(DMatrix):
+class _RefMixIn:
+    @property
+    def ref(self) -> Optional[weakref.ReferenceType]:
+        """Internal method for retrieving a reference to the training DMatrix."""
+        return self._ref
+
+    @ref.setter
+    def ref(self, ref: weakref.ReferenceType) -> None:
+        self._ref = ref
+
+
+class QuantileDMatrix(DMatrix, _RefMixIn):
     """A DMatrix variant that generates quantilized data directly from input for the
     ``hist`` tree method. This DMatrix is primarily designed to save memory in training
     by avoiding intermediate storage. Set ``max_bin`` to control the number of bins
@@ -1644,7 +1655,7 @@ def _init(
             self.ref = weakref.ref(ref)
 
 
-class ExtMemQuantileDMatrix(DMatrix):
+class ExtMemQuantileDMatrix(DMatrix, _RefMixIn):
     """The external memory version of the :py:class:`QuantileDMatrix`.
 
     See :doc:`/tutorials/external_memory` for explanation and usage examples, and
@@ -1742,7 +1753,6 @@ def _init(
         _check_call(ret)
         self.handle = handle
 
-        self._ref: Optional[weakref.ReferenceType] = None
         if ref is not None:
             self.ref = weakref.ref(ref)
 

python-package/xgboost/training.py

@@ -3,6 +3,7 @@
 """Training Library containing training routines."""
 import copy
 import os
+import weakref
 from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast
 
 import numpy as np
@@ -146,9 +147,13 @@ def train(
 
     callbacks = [] if callbacks is None else copy.copy(list(callbacks))
     evals = list(evals) if evals else []
+
     for va, _ in evals:
+        if not isinstance(va, DMatrix):
+            raise TypeError("Invalid type for the `evals`,")
+
         if hasattr(va, "ref") and va.ref is not None:
-            if va is not dtrain and va.ref is not dtrain:
+            if va is not dtrain and va.ref is not weakref.ref(dtrain):
                 raise ValueError(
                     "Training dataset should be used as a reference when constructing "
                     "the `QuantileDMatrix` for evaluation."