Support numpy array interface (#6998)

python-package/xgboost/data.py

@@ -116,11 +116,6 @@ def _maybe_np_slice(data, dtype):
     '''
     try:
         if not data.flags.c_contiguous:
-            warnings.warn(
-                "Use of np.ndarray subsets (sliced data) is not recommended " +
-                "because it will generate extra copies and increase " +
-                "memory consumption. Consider using np.ascontiguousarray to " +
-                "make the array contiguous.")
             data = np.array(data, copy=True, dtype=dtype)
         else:
             data = np.array(data, copy=False, dtype=dtype)
@@ -130,44 +125,28 @@ def _maybe_np_slice(data, dtype):
     return data
 
 
-def _transform_np_array(data: np.ndarray) -> np.ndarray:
-    if not isinstance(data, np.ndarray) and hasattr(data, '__array__'):
-        data = np.array(data, copy=False)
-    if len(data.shape) != 2:
-        raise ValueError('Expecting 2 dimensional numpy.ndarray, got: ',
-                         data.shape)
-    # flatten the array by rows and ensure it is float32.  we try to avoid
-    # data copies if possible (reshape returns a view when possible and we
-    # explicitly tell np.array to try and avoid copying)
-    flatten = np.array(data.reshape(data.size), copy=False,
-                       dtype=np.float32)
-    flatten = _maybe_np_slice(flatten, np.float32)
-    _check_complex(data)
-    return flatten
-
-
 def _from_numpy_array(data, missing, nthread, feature_names, feature_types):
     """Initialize data from a 2-D numpy matrix.
 
-    If ``mat`` does not have ``order='C'`` (aka row-major) or is
-    not contiguous, a temporary copy will be made.
-
-    If ``mat`` does not have ``dtype=numpy.float32``, a temporary copy will
-    be made.
-
-    So there could be as many as two temporary data copies; be mindful of
-    input layout and type if memory use is a concern.
-
     """
-    flatten: np.ndarray = _transform_np_array(data)
+    if len(data.shape) != 2:
+        raise ValueError(
+            "Expecting 2 dimensional numpy.ndarray, got: ", data.shape
+        )
+    data, _ = _ensure_np_dtype(data, data.dtype)
     handle = ctypes.c_void_p()
-    _check_call(_LIB.XGDMatrixCreateFromMat_omp(
-        flatten.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
-        c_bst_ulong(data.shape[0]),
-        c_bst_ulong(data.shape[1]),
-        ctypes.c_float(missing),
-        ctypes.byref(handle),
-        ctypes.c_int(nthread)))
+    args = {
+        "missing": float(missing),
+        "nthread": int(nthread),
+    }
+    config = bytes(json.dumps(args), "utf-8")
+    _check_call(
+        _LIB.XGDMatrixCreateFromArray(
+            _array_interface(data),
+            config,
+            ctypes.byref(handle),
+        )
+    )
     return handle, feature_names, feature_types