From 69a17d51141bd2457142c8309f57494ab2d97562 Mon Sep 17 00:00:00 2001
From: Jiaming Yuan <jm.yuan@outlook.com>
Date: Tue, 20 Feb 2024 22:34:22 +0800
Subject: [PATCH] Fix with None input. (#10052)

---
 python-package/xgboost/core.py     | 19 ++++++++++---------
 python-package/xgboost/data.py     |  4 ++--
 tests/python-gpu/test_from_cupy.py |  5 ++++-
 tests/python/test_with_sklearn.py  | 13 +++++++++++++
 4 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index 27331d3de..f19078224 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -861,9 +861,9 @@ class DMatrix:  # pylint: disable=too-many-instance-attributes,too-many-public-m
         self.nthread = nthread if nthread is not None else -1
         self.silent = silent
 
-        # force into void_p, mac need to pass things in as void_p
-        if data is None:
-            self.handle: Optional[ctypes.c_void_p] = None
+        if isinstance(data, ctypes.c_void_p):
+            # Used for constructing DMatrix slice.
+            self.handle = data
             return
 
         from .data import _is_iter, dispatch_data_backend
@@ -925,9 +925,10 @@ class DMatrix:  # pylint: disable=too-many-instance-attributes,too-many-public-m
         self.handle = handle
 
     def __del__(self) -> None:
-        if hasattr(self, "handle") and self.handle:
+        if hasattr(self, "handle"):
+            assert self.handle is not None
             _check_call(_LIB.XGDMatrixFree(self.handle))
-            self.handle = None
+            del self.handle
 
     @_deprecate_positional_args
     def set_info(
@@ -1281,19 +1282,19 @@ class DMatrix:  # pylint: disable=too-many-instance-attributes,too-many-public-m
         """
         from .data import _maybe_np_slice
 
-        res = DMatrix(None)
-        res.handle = ctypes.c_void_p()
+        handle = ctypes.c_void_p()
+
         rindex = _maybe_np_slice(rindex, dtype=np.int32)
         _check_call(
             _LIB.XGDMatrixSliceDMatrixEx(
                 self.handle,
                 c_array(ctypes.c_int, rindex),
                 c_bst_ulong(len(rindex)),
-                ctypes.byref(res.handle),
+                ctypes.byref(handle),
                 ctypes.c_int(1 if allow_groups else 0),
             )
         )
-        return res
+        return DMatrix(handle)
 
     @property
     def feature_names(self) -> Optional[FeatureNames]:
diff --git a/python-package/xgboost/data.py b/python-package/xgboost/data.py
index 49a0f43b7..07a08dc5f 100644
--- a/python-package/xgboost/data.py
+++ b/python-package/xgboost/data.py
@@ -1053,10 +1053,10 @@ def _is_dlpack(data: DataType) -> bool:
 
 
 def _transform_dlpack(data: DataType) -> bool:
-    from cupy import fromDlpack  # pylint: disable=E0401
+    from cupy import from_dlpack  # pylint: disable=E0401
 
     assert "used_dltensor" not in str(data)
-    data = fromDlpack(data)
+    data = from_dlpack(data)
     return data
 
 
diff --git a/tests/python-gpu/test_from_cupy.py b/tests/python-gpu/test_from_cupy.py
index 79814a1bb..85d54c78d 100644
--- a/tests/python-gpu/test_from_cupy.py
+++ b/tests/python-gpu/test_from_cupy.py
@@ -202,7 +202,10 @@ class TestFromCupy:
         n = 100
         X = cp.random.random((n, 2))
         m = xgb.QuantileDMatrix(X.toDlpack())
-        with pytest.raises(xgb.core.XGBoostError):
+
+        with pytest.raises(
+            xgb.core.XGBoostError, match="Slicing DMatrix is not supported"
+        ):
             m.slice(rindex=[0, 1, 2])
 
     @pytest.mark.skipif(**tm.no_cupy())
diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py
index 344628e4f..ede70bb8b 100644
--- a/tests/python/test_with_sklearn.py
+++ b/tests/python/test_with_sklearn.py
@@ -1456,3 +1456,16 @@ def test_intercept() -> None:
     result = reg.intercept_
     assert result.dtype == np.float32
     assert result[0] < 0.5
+
+
+def test_fit_none() -> None:
+    with pytest.raises(TypeError, match="NoneType"):
+        xgb.XGBClassifier().fit(None, [0, 1])
+
+    X = rng.normal(size=4).reshape(2, 2)
+
+    with pytest.raises(ValueError, match="Invalid classes"):
+        xgb.XGBClassifier().fit(X, None)
+
+    with pytest.raises(ValueError, match="labels"):
+        xgb.XGBRegressor().fit(X, None)