def _from_dt_df(data, missing, nthread, feature_names, feature_types):
data, feature_names, feature_types = _transform_dt_df(
data, feature_names, feature_types, None, None)
ptrs = (ctypes.c_void_p * data.ncols)()
if hasattr(data, "internal") and hasattr(data.internal, "column"):
# datatable>0.8.0
for icol in range(data.ncols):
col = data.internal.column(icol)
ptr = col.data_pointer
ptrs[icol] = ctypes.c_void_p(ptr)
else:
# datatable<=0.8.0
from datatable.internal import \
frame_column_data_r # pylint: disable=no-name-in-module
for icol in range(data.ncols):
ptrs[icol] = frame_column_data_r(data, icol)
# always return stypes for dt ingestion
feature_type_strings = (ctypes.c_char_p * data.ncols)()
for icol in range(data.ncols):
feature_type_strings[icol] = ctypes.c_char_p(
data.stypes[icol].name.encode('utf-8'))
_warn_unused_missing(data, missing)
handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromDT(
ptrs, feature_type_strings,
c_bst_ulong(data.shape[0]),
c_bst_ulong(data.shape[1]),
ctypes.byref(handle),
ctypes.c_int(nthread)))
return handle, feature_names, feature_types
def _from_dt_df(
data: DataType,
missing: Optional[FloatCompatible],
nthread: int,
feature_names: Optional[FeatureNames],
feature_types: Optional[FeatureTypes],
enable_categorical: bool,
) -> DispatchedDataBackendReturnType:
if enable_categorical:
raise ValueError("categorical data in datatable is not supported yet.")
data, feature_names, feature_types = _transform_dt_df(
data, feature_names, feature_types, None, None)
ptrs = (ctypes.c_void_p * data.ncols)()
if hasattr(data, "internal") and hasattr(data.internal, "column"):
# datatable>0.8.0
for icol in range(data.ncols):
col = data.internal.column(icol)
ptr = col.data_pointer
ptrs[icol] = ctypes.c_void_p(ptr)
else:
# datatable<=0.8.0
from datatable.internal import \
frame_column_data_r # pylint: disable=no-name-in-module
for icol in range(data.ncols):
ptrs[icol] = frame_column_data_r(data, icol)
# always return stypes for dt ingestion
feature_type_strings = (ctypes.c_char_p * data.ncols)()
for icol in range(data.ncols):
feature_type_strings[icol] = ctypes.c_char_p(
data.stypes[icol].name.encode('utf-8'))
_warn_unused_missing(data, missing)
handle = ctypes.c_void_p()
_check_call(
_LIB.XGDMatrixCreateFromDT(ptrs, feature_type_strings,
c_bst_ulong(data.shape[0]),
c_bst_ulong(data.shape[1]),
ctypes.byref(handle),
ctypes.c_int(nthread)))
return handle, feature_names, feature_types
