Fix the new API.

python-package/xgboost/data.py

@@ -539,19 +539,27 @@ def _to_data_type(dtype: str, name: str):
     return dtype_map[dtype]
 
 
+def _validate_meta_shape(data):
+    if hasattr(data, 'shape'):
+        assert len(data.shape) == 1 or (
+            len(data.shape) == 2 and
+            (data.shape[1] == 0 or data.shape[1] == 1))
+
+
 def _meta_from_numpy(data, field, dtype, handle):
     data = _maybe_np_slice(data, dtype)
     interface = data.__array_interface__
     assert interface.get('mask', None) is None, 'Masked array is not supported'
     size = data.shape[0]
+
     c_type = _to_data_type(str(data.dtype), field)
-    data = interface['data']
-    data = ctypes.c_void_p(data[0])
+    ptr = interface['data'][0]
+    ptr = ctypes.c_void_p(ptr)
     _check_call(_LIB.XGDMatrixSetDenseInfo(
         handle,
         c_str(field),
-        data,
-        size,
+        ptr,
+        c_bst_ulong(size),
         c_type
     ))
 
@@ -603,6 +611,7 @@ def _meta_from_dt(data, field, dtype, handle):
 def dispatch_meta_backend(matrix: DMatrix, data, name: str, dtype: str = None):
     '''Dispatch for meta info.'''
     handle = matrix.handle
+    _validate_meta_shape(data)
     if data is None:
         return
     if _is_list(data):

tests/python/test_dmatrix.py

@@ -96,18 +96,13 @@ def test_np_view(self):
         assert (from_view == from_array).all()
 
     def test_slice(self):
-        # 2887052510386eb7d12e09c859529a4f2b01ba35b847c807f95cbaddbb5eea7a
         X = rng.randn(100, 100)
-        # 85a40616f6748d98e59baf2961b655e0ebb2fc8ac298fc638a173e434073a0f9
         y = rng.randint(low=0, high=3, size=100)
-        # ab7b1162766f953935c244dd73d663ac0b79e4bd0a914a64e5b0fe66ae55b7ef
-        # failed:
-        # 33d8eee9310c7f6ff57f0c876b5977f39f7e450eb7c71513cc4c133c57921a6
         d = xgb.DMatrix(X, y)
         np.testing.assert_equal(d.get_label(), y.astype(np.float32))
 
-        # fw = rng.uniform(size=100).astype(np.float32)
-        # d.set_info(feature_weights=fw)
+        fw = rng.uniform(size=100).astype(np.float32)
+        d.set_info(feature_weights=fw)
 
         eval_res_0 = {}
         booster = xgb.train(
@@ -127,12 +122,7 @@ def test_slice(self):
         d.set_base_margin(predt)
 
         ridxs = [1, 2, 3, 4, 5, 6]
-        # failed:
-        # f3459dc754e30ff09e91c9660789cef53d998d6256f8ee81cc9c304cc54fbf40
-        # passed:
-        # ab7b1162766f953935c244dd73d663ac0b79e4bd0a914a64e5b0fe66ae55b7ef
         sliced = d.slice(ridxs)
-        # np.testing.assert_equal(sliced.get_float_info('feature_weights'), fw)
 
         sliced_margin = sliced.get_float_info('base_margin')
         assert sliced_margin.shape[0] == len(ridxs) * 3