Take datatable as row major input. (#8472)

* Take datatable as row major input.

Try to avoid a transform with dense table.

python-package/xgboost/data.py

@@ -44,8 +44,9 @@ _matrix_meta = {"base_margin", "label"}
 def _warn_unused_missing(data: DataType, missing: Optional[FloatCompatible]) -> None:
     if (missing is not None) and (not np.isnan(missing)):
         warnings.warn(
-            '`missing` is not used for current input data type:' +
-            str(type(data)), UserWarning)
+            "`missing` is not used for current input data type:" + str(type(data)),
+            UserWarning,
+        )
 
 
 def _check_complex(data: DataType) -> None:
@@ -459,12 +460,9 @@ def _from_pandas_series(
 
 
 def _is_dt_df(data: DataType) -> bool:
-    return lazy_isinstance(data, 'datatable', 'Frame') or \
-        lazy_isinstance(data, 'datatable', 'DataTable')
-
-
-_dt_type_mapper = {'bool': 'bool', 'int': 'int', 'real': 'float'}
-_dt_type_mapper2 = {'bool': 'i', 'int': 'int', 'real': 'float'}
+    return lazy_isinstance(data, "datatable", "Frame") or lazy_isinstance(
+        data, "datatable", "DataTable"
+    )
 
 
 def _transform_dt_df(
@@ -475,8 +473,10 @@ def _transform_dt_df(
     meta_type: Optional[NumpyDType] = None,
 ) -> Tuple[np.ndarray, Optional[FeatureNames], Optional[FeatureTypes]]:
     """Validate feature names and types if data table"""
+    _dt_type_mapper = {"bool": "bool", "int": "int", "real": "float"}
+    _dt_type_mapper2 = {"bool": "i", "int": "int", "real": "float"}
     if meta and data.shape[1] > 1:
-        raise ValueError('DataTable for meta info cannot have multiple columns')
+        raise ValueError("DataTable for meta info cannot have multiple columns")
     if meta:
         meta_type = "float" if meta_type is None else meta_type
         # below requires new dt version
@@ -485,23 +485,23 @@ def _transform_dt_df(
         return data, None, None
 
     data_types_names = tuple(lt.name for lt in data.ltypes)
-    bad_fields = [data.names[i]
-                  for i, type_name in enumerate(data_types_names)
-                  if type_name not in _dt_type_mapper]
+    bad_fields = [
+        data.names[i]
+        for i, type_name in enumerate(data_types_names)
+        if type_name not in _dt_type_mapper
+    ]
     if bad_fields:
         msg = """DataFrame.types for data must be int, float or bool.
                 Did not expect the data types in fields """
-        raise ValueError(msg + ', '.join(bad_fields))
+        raise ValueError(msg + ", ".join(bad_fields))
 
     if feature_names is None and meta is None:
         feature_names = data.names
 
         # always return stypes for dt ingestion
         if feature_types is not None:
-            raise ValueError(
-                'DataTable has own feature types, cannot pass them in.')
-        feature_types = np.vectorize(_dt_type_mapper2.get)(
-            data_types_names).tolist()
+            raise ValueError("DataTable has own feature types, cannot pass them in.")
+        feature_types = np.vectorize(_dt_type_mapper2.get)(data_types_names).tolist()
 
     return data, feature_names, feature_types
 
@@ -517,7 +517,8 @@ def _from_dt_df(
     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)
+        data, feature_names, feature_types, None, None
+    )
 
     ptrs = (ctypes.c_void_p * data.ncols)()
     if hasattr(data, "internal") and hasattr(data.internal, "column"):
@@ -531,6 +532,7 @@ def _from_dt_df(
         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)
 
@@ -538,16 +540,21 @@ def _from_dt_df(
     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'))
+            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)))
+    _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