1339 lines
42 KiB
Python
1339 lines
42 KiB
Python
# pylint: disable=too-many-arguments, too-many-branches, too-many-lines
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# pylint: disable=too-many-return-statements, import-error
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"""Data dispatching for DMatrix."""
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import ctypes
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import json
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import os
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import warnings
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from typing import Any, Callable, Iterator, List, Optional, Sequence, Tuple, Union, cast
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import numpy as np
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from ._typing import (
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CupyT,
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DataType,
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FeatureNames,
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FeatureTypes,
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FloatCompatible,
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NumpyDType,
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PandasDType,
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c_bst_ulong,
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)
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from .compat import DataFrame, lazy_isinstance
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from .core import (
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_LIB,
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DataIter,
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DataSplitMode,
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DMatrix,
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_check_call,
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_cuda_array_interface,
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_ProxyDMatrix,
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c_str,
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from_pystr_to_cstr,
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make_jcargs,
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)
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DispatchedDataBackendReturnType = Tuple[
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ctypes.c_void_p, Optional[FeatureNames], Optional[FeatureTypes]
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]
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CAT_T = "c"
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# meta info that can be a matrix instead of vector.
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_matrix_meta = {"base_margin", "label"}
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def _warn_unused_missing(data: DataType, missing: Optional[FloatCompatible]) -> None:
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if (missing is not None) and (not np.isnan(missing)):
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warnings.warn(
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"`missing` is not used for current input data type:" + str(type(data)),
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UserWarning,
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)
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def _check_data_shape(data: DataType) -> None:
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if hasattr(data, "shape") and len(data.shape) != 2:
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raise ValueError("Please reshape the input data into 2-dimensional matrix.")
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def _is_scipy_csr(data: DataType) -> bool:
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try:
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import scipy.sparse
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except ImportError:
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return False
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return isinstance(data, scipy.sparse.csr_matrix)
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def _array_interface(data: np.ndarray) -> bytes:
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assert (
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data.dtype.hasobject is False
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), "Input data contains `object` dtype. Expecting numeric data."
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interface = data.__array_interface__
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if "mask" in interface:
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interface["mask"] = interface["mask"].__array_interface__
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interface_str = bytes(json.dumps(interface), "utf-8")
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return interface_str
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def transform_scipy_sparse(data: DataType, is_csr: bool) -> DataType:
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"""Ensure correct data alignment and data type for scipy sparse inputs. Input should
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be either csr or csc matrix.
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"""
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from scipy.sparse import csc_matrix, csr_matrix
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if len(data.indices) != len(data.data):
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raise ValueError(f"length mismatch: {len(data.indices)} vs {len(data.data)}")
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indptr, _ = _ensure_np_dtype(data.indptr, data.indptr.dtype)
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indices, _ = _ensure_np_dtype(data.indices, data.indices.dtype)
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values, _ = _ensure_np_dtype(data.data, data.data.dtype)
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if (
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indptr is not data.indptr
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or indices is not data.indices
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or values is not data.data
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):
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if is_csr:
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data = csr_matrix((values, indices, indptr), shape=data.shape)
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else:
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data = csc_matrix((values, indices, indptr), shape=data.shape)
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return data
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def _from_scipy_csr(
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data: DataType,
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missing: FloatCompatible,
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nthread: int,
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feature_names: Optional[FeatureNames],
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feature_types: Optional[FeatureTypes],
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) -> DispatchedDataBackendReturnType:
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"""Initialize data from a CSR matrix."""
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handle = ctypes.c_void_p()
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data = transform_scipy_sparse(data, True)
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_check_call(
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_LIB.XGDMatrixCreateFromCSR(
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_array_interface(data.indptr),
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_array_interface(data.indices),
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_array_interface(data.data),
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c_bst_ulong(data.shape[1]),
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make_jcargs(missing=float(missing), nthread=int(nthread)),
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ctypes.byref(handle),
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)
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)
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return handle, feature_names, feature_types
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def _is_scipy_csc(data: DataType) -> bool:
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try:
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import scipy.sparse
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except ImportError:
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return False
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return isinstance(data, scipy.sparse.csc_matrix)
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def _from_scipy_csc(
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data: DataType,
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missing: FloatCompatible,
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nthread: int,
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feature_names: Optional[FeatureNames],
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feature_types: Optional[FeatureTypes],
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) -> DispatchedDataBackendReturnType:
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"""Initialize data from a CSC matrix."""
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handle = ctypes.c_void_p()
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transform_scipy_sparse(data, False)
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_check_call(
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_LIB.XGDMatrixCreateFromCSC(
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_array_interface(data.indptr),
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_array_interface(data.indices),
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_array_interface(data.data),
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c_bst_ulong(data.shape[0]),
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make_jcargs(missing=float(missing), nthread=int(nthread)),
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ctypes.byref(handle),
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)
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)
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return handle, feature_names, feature_types
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def _is_scipy_coo(data: DataType) -> bool:
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try:
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import scipy.sparse
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except ImportError:
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return False
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return isinstance(data, scipy.sparse.coo_matrix)
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def _is_numpy_array(data: DataType) -> bool:
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return isinstance(data, (np.ndarray, np.matrix))
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def _ensure_np_dtype(
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data: DataType, dtype: Optional[NumpyDType]
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) -> Tuple[np.ndarray, Optional[NumpyDType]]:
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if data.dtype.hasobject or data.dtype in [np.float16, np.bool_]:
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dtype = np.float32
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data = data.astype(dtype, copy=False)
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if not data.flags.aligned:
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data = np.require(data, requirements="A")
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return data, dtype
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def _maybe_np_slice(data: DataType, dtype: Optional[NumpyDType]) -> np.ndarray:
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"""Handle numpy slice. This can be removed if we use __array_interface__."""
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try:
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if not data.flags.c_contiguous:
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data = np.array(data, copy=True, dtype=dtype)
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else:
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data = np.array(data, copy=False, dtype=dtype)
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except AttributeError:
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data = np.array(data, copy=False, dtype=dtype)
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data, dtype = _ensure_np_dtype(data, dtype)
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return data
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def _from_numpy_array(
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data: DataType,
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missing: FloatCompatible,
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nthread: int,
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feature_names: Optional[FeatureNames],
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feature_types: Optional[FeatureTypes],
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) -> DispatchedDataBackendReturnType:
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"""Initialize data from a 2-D numpy matrix."""
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_check_data_shape(data)
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data, _ = _ensure_np_dtype(data, data.dtype)
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handle = ctypes.c_void_p()
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_check_call(
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_LIB.XGDMatrixCreateFromDense(
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_array_interface(data),
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make_jcargs(missing=float(missing), nthread=int(nthread)),
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ctypes.byref(handle),
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)
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)
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return handle, feature_names, feature_types
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def _is_pandas_df(data: DataType) -> bool:
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try:
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import pandas as pd
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except ImportError:
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return False
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return isinstance(data, pd.DataFrame)
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def _is_modin_df(data: DataType) -> bool:
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try:
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import modin.pandas as pd
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except ImportError:
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return False
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return isinstance(data, pd.DataFrame)
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_pandas_dtype_mapper = {
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"int8": "int",
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"int16": "int",
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"int32": "int",
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"int64": "int",
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"uint8": "int",
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"uint16": "int",
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"uint32": "int",
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"uint64": "int",
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"float16": "float",
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"float32": "float",
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"float64": "float",
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"bool": "i",
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}
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# nullable types
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pandas_nullable_mapper = {
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"Int8": "int",
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"Int16": "int",
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"Int32": "int",
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"Int64": "int",
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"UInt8": "i",
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"UInt16": "i",
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"UInt32": "i",
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"UInt64": "i",
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"Float32": "float",
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"Float64": "float",
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"boolean": "i",
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}
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pandas_pyarrow_mapper = {
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"int8[pyarrow]": "i",
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"int16[pyarrow]": "i",
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"int32[pyarrow]": "i",
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"int64[pyarrow]": "i",
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"uint8[pyarrow]": "i",
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"uint16[pyarrow]": "i",
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"uint32[pyarrow]": "i",
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"uint64[pyarrow]": "i",
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"float[pyarrow]": "float",
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"float32[pyarrow]": "float",
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"double[pyarrow]": "float",
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"float64[pyarrow]": "float",
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"bool[pyarrow]": "i",
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}
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_pandas_dtype_mapper.update(pandas_nullable_mapper)
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_pandas_dtype_mapper.update(pandas_pyarrow_mapper)
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_ENABLE_CAT_ERR = (
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"When categorical type is supplied, The experimental DMatrix parameter"
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"`enable_categorical` must be set to `True`."
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)
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def _invalid_dataframe_dtype(data: DataType) -> None:
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# pandas series has `dtypes` but it's just a single object
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# cudf series doesn't have `dtypes`.
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if hasattr(data, "dtypes") and hasattr(data.dtypes, "__iter__"):
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bad_fields = [
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f"{data.columns[i]}: {dtype}"
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for i, dtype in enumerate(data.dtypes)
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if dtype.name not in _pandas_dtype_mapper
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]
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err = " Invalid columns:" + ", ".join(bad_fields)
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else:
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err = ""
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type_err = "DataFrame.dtypes for data must be int, float, bool or category."
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msg = f"""{type_err} {_ENABLE_CAT_ERR} {err}"""
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raise ValueError(msg)
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def pandas_feature_info(
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data: DataFrame,
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meta: Optional[str],
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feature_names: Optional[FeatureNames],
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feature_types: Optional[FeatureTypes],
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enable_categorical: bool,
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) -> Tuple[Optional[FeatureNames], Optional[FeatureTypes]]:
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"""Handle feature info for pandas dataframe."""
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import pandas as pd
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from pandas.api.types import is_categorical_dtype, is_sparse
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# handle feature names
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if feature_names is None and meta is None:
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if isinstance(data.columns, pd.MultiIndex):
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feature_names = [" ".join([str(x) for x in i]) for i in data.columns]
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elif isinstance(data.columns, (pd.Index, pd.RangeIndex)):
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feature_names = list(map(str, data.columns))
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else:
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feature_names = data.columns.format()
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# handle feature types
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if feature_types is None and meta is None:
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feature_types = []
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for dtype in data.dtypes:
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if is_sparse(dtype):
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feature_types.append(_pandas_dtype_mapper[dtype.subtype.name])
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elif (
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is_categorical_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)
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) and enable_categorical:
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feature_types.append(CAT_T)
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else:
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feature_types.append(_pandas_dtype_mapper[dtype.name])
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return feature_names, feature_types
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|
|
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def is_nullable_dtype(dtype: PandasDType) -> bool:
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"""Whether dtype is a pandas nullable type."""
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from pandas.api.types import (
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is_bool_dtype,
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is_categorical_dtype,
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is_float_dtype,
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is_integer_dtype,
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)
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is_int = is_integer_dtype(dtype) and dtype.name in pandas_nullable_mapper
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# np.bool has alias `bool`, while pd.BooleanDtype has `boolean`.
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is_bool = is_bool_dtype(dtype) and dtype.name == "boolean"
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is_float = is_float_dtype(dtype) and dtype.name in pandas_nullable_mapper
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return is_int or is_bool or is_float or is_categorical_dtype(dtype)
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|
|
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def is_pa_ext_dtype(dtype: Any) -> bool:
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"""Return whether dtype is a pyarrow extension type for pandas"""
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return hasattr(dtype, "pyarrow_dtype")
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|
|
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def is_pa_ext_categorical_dtype(dtype: Any) -> bool:
|
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"""Check whether dtype is a dictionary type."""
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return lazy_isinstance(
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getattr(dtype, "pyarrow_dtype", None), "pyarrow.lib", "DictionaryType"
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)
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|
|
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def pandas_cat_null(data: DataFrame) -> DataFrame:
|
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"""Handle categorical dtype and nullable extension types from pandas."""
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import pandas as pd
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from pandas.api.types import is_categorical_dtype
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# handle category codes and nullable.
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cat_columns = []
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nul_columns = []
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# avoid an unnecessary conversion if possible
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for col, dtype in zip(data.columns, data.dtypes):
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if is_categorical_dtype(dtype):
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cat_columns.append(col)
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elif is_pa_ext_categorical_dtype(dtype):
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raise ValueError(
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"pyarrow dictionary type is not supported. Use pandas category instead."
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)
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elif is_nullable_dtype(dtype):
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nul_columns.append(col)
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if cat_columns or nul_columns:
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# Avoid transformation due to: PerformanceWarning: DataFrame is highly
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# fragmented
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transformed = data.copy(deep=False)
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else:
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transformed = data
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|
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def cat_codes(ser: pd.Series) -> pd.Series:
|
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if is_categorical_dtype(ser.dtype):
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return ser.cat.codes
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assert is_pa_ext_categorical_dtype(ser.dtype)
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# Not yet supported, the index is not ordered for some reason. Alternately:
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# `combine_chunks().to_pandas().cat.codes`. The result is the same.
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return ser.array.__arrow_array__().combine_chunks().dictionary_encode().indices
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|
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if cat_columns:
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# DF doesn't have the cat attribute, as a result, we use apply here
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transformed[cat_columns] = (
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transformed[cat_columns]
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.apply(cat_codes)
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.astype(np.float32)
|
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.replace(-1.0, np.NaN)
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)
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if nul_columns:
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transformed[nul_columns] = transformed[nul_columns].astype(np.float32)
|
|
|
|
# TODO(jiamingy): Investigate the possibility of using dataframe protocol or arrow
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# IPC format for pandas so that we can apply the data transformation inside XGBoost
|
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# for better memory efficiency.
|
|
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return transformed
|
|
|
|
|
|
def pandas_ext_num_types(data: DataFrame) -> DataFrame:
|
|
"""Experimental suppport for handling pyarrow extension numeric types."""
|
|
import pandas as pd
|
|
import pyarrow as pa
|
|
|
|
for col, dtype in zip(data.columns, data.dtypes):
|
|
if not is_pa_ext_dtype(dtype):
|
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continue
|
|
# No copy, callstack:
|
|
# pandas.core.internals.managers.SingleBlockManager.array_values()
|
|
# pandas.core.internals.blocks.EABackedBlock.values
|
|
d_array: pd.arrays.ArrowExtensionArray = data[col].array
|
|
# no copy in __arrow_array__
|
|
# ArrowExtensionArray._data is a chunked array
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|
aa: pa.ChunkedArray = d_array.__arrow_array__()
|
|
chunk: pa.Array = aa.combine_chunks()
|
|
# Alternately, we can use chunk.buffers(), which returns a list of buffers and
|
|
# we need to concatenate them ourselves.
|
|
arr = chunk.__array__()
|
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data[col] = arr
|
|
return data
|
|
|
|
|
|
def _transform_pandas_df(
|
|
data: DataFrame,
|
|
enable_categorical: bool,
|
|
feature_names: Optional[FeatureNames] = None,
|
|
feature_types: Optional[FeatureTypes] = None,
|
|
meta: Optional[str] = None,
|
|
meta_type: Optional[NumpyDType] = None,
|
|
) -> Tuple[np.ndarray, Optional[FeatureNames], Optional[FeatureTypes]]:
|
|
from pandas.api.types import is_categorical_dtype, is_sparse
|
|
|
|
pyarrow_extension = False
|
|
for dtype in data.dtypes:
|
|
if not (
|
|
(dtype.name in _pandas_dtype_mapper)
|
|
or is_sparse(dtype)
|
|
or (is_categorical_dtype(dtype) and enable_categorical)
|
|
or is_pa_ext_dtype(dtype)
|
|
):
|
|
_invalid_dataframe_dtype(data)
|
|
if is_pa_ext_dtype(dtype):
|
|
pyarrow_extension = True
|
|
|
|
feature_names, feature_types = pandas_feature_info(
|
|
data, meta, feature_names, feature_types, enable_categorical
|
|
)
|
|
|
|
transformed = pandas_cat_null(data)
|
|
if pyarrow_extension:
|
|
if transformed is data:
|
|
transformed = data.copy(deep=False)
|
|
transformed = pandas_ext_num_types(transformed)
|
|
|
|
if meta and len(data.columns) > 1 and meta not in _matrix_meta:
|
|
raise ValueError(f"DataFrame for {meta} cannot have multiple columns")
|
|
|
|
dtype = meta_type if meta_type else np.float32
|
|
arr: np.ndarray = transformed.values
|
|
if meta_type:
|
|
arr = arr.astype(dtype)
|
|
return arr, feature_names, feature_types
|
|
|
|
|
|
def _from_pandas_df(
|
|
data: DataFrame,
|
|
enable_categorical: bool,
|
|
missing: FloatCompatible,
|
|
nthread: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
) -> DispatchedDataBackendReturnType:
|
|
data, feature_names, feature_types = _transform_pandas_df(
|
|
data, enable_categorical, feature_names, feature_types
|
|
)
|
|
return _from_numpy_array(data, missing, nthread, feature_names, feature_types)
|
|
|
|
|
|
def _is_pandas_series(data: DataType) -> bool:
|
|
try:
|
|
import pandas as pd
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, pd.Series)
|
|
|
|
|
|
def _meta_from_pandas_series(
|
|
data: DataType, name: str, dtype: Optional[NumpyDType], handle: ctypes.c_void_p
|
|
) -> None:
|
|
"""Help transform pandas series for meta data like labels"""
|
|
data = data.values.astype("float")
|
|
from pandas.api.types import is_sparse
|
|
|
|
if is_sparse(data):
|
|
data = data.to_dense() # type: ignore
|
|
assert len(data.shape) == 1 or data.shape[1] == 0 or data.shape[1] == 1
|
|
_meta_from_numpy(data, name, dtype, handle)
|
|
|
|
|
|
def _is_modin_series(data: DataType) -> bool:
|
|
try:
|
|
import modin.pandas as pd
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, pd.Series)
|
|
|
|
|
|
def _from_pandas_series(
|
|
data: DataType,
|
|
missing: FloatCompatible,
|
|
nthread: int,
|
|
enable_categorical: bool,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
) -> DispatchedDataBackendReturnType:
|
|
from pandas.api.types import is_categorical_dtype
|
|
|
|
if (data.dtype.name not in _pandas_dtype_mapper) and not (
|
|
is_categorical_dtype(data.dtype) and enable_categorical
|
|
):
|
|
_invalid_dataframe_dtype(data)
|
|
if enable_categorical and is_categorical_dtype(data.dtype):
|
|
data = data.cat.codes
|
|
return _from_numpy_array(
|
|
data.values.reshape(data.shape[0], 1).astype("float"),
|
|
missing,
|
|
nthread,
|
|
feature_names,
|
|
feature_types,
|
|
)
|
|
|
|
|
|
def _is_dt_df(data: DataType) -> bool:
|
|
return lazy_isinstance(data, "datatable", "Frame") or lazy_isinstance(
|
|
data, "datatable", "DataTable"
|
|
)
|
|
|
|
|
|
def _transform_dt_df(
|
|
data: DataType,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
meta: Optional[str] = None,
|
|
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")
|
|
if meta:
|
|
meta_type = "float" if meta_type is None else meta_type
|
|
# below requires new dt version
|
|
# extract first column
|
|
data = data.to_numpy()[:, 0].astype(meta_type)
|
|
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
|
|
]
|
|
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))
|
|
|
|
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()
|
|
|
|
return data, 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
|
|
|
|
|
|
def _is_arrow(data: DataType) -> bool:
|
|
try:
|
|
import pyarrow as pa
|
|
from pyarrow import dataset as arrow_dataset
|
|
|
|
return isinstance(data, (pa.Table, arrow_dataset.Dataset))
|
|
except ImportError:
|
|
return False
|
|
|
|
|
|
def record_batch_data_iter(data_iter: Iterator) -> Callable:
|
|
"""Data iterator used to ingest Arrow columnar record batches. We are not using
|
|
class DataIter because it is only intended for building Device DMatrix and external
|
|
memory DMatrix.
|
|
|
|
"""
|
|
from pyarrow.cffi import ffi
|
|
|
|
c_schemas: List[ffi.CData] = []
|
|
c_arrays: List[ffi.CData] = []
|
|
|
|
def _next(data_handle: int) -> int:
|
|
from pyarrow.cffi import ffi
|
|
|
|
try:
|
|
batch = next(data_iter)
|
|
c_schemas.append(ffi.new("struct ArrowSchema*"))
|
|
c_arrays.append(ffi.new("struct ArrowArray*"))
|
|
ptr_schema = int(ffi.cast("uintptr_t", c_schemas[-1]))
|
|
ptr_array = int(ffi.cast("uintptr_t", c_arrays[-1]))
|
|
# pylint: disable=protected-access
|
|
batch._export_to_c(ptr_array, ptr_schema)
|
|
_check_call(
|
|
_LIB.XGImportArrowRecordBatch(
|
|
ctypes.c_void_p(data_handle),
|
|
ctypes.c_void_p(ptr_array),
|
|
ctypes.c_void_p(ptr_schema),
|
|
)
|
|
)
|
|
return 1
|
|
except StopIteration:
|
|
return 0
|
|
|
|
return _next
|
|
|
|
|
|
def _from_arrow(
|
|
data: DataType,
|
|
missing: FloatCompatible,
|
|
nthread: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
enable_categorical: bool,
|
|
) -> DispatchedDataBackendReturnType:
|
|
import pyarrow as pa
|
|
|
|
if not all(
|
|
pa.types.is_integer(t) or pa.types.is_floating(t) for t in data.schema.types
|
|
):
|
|
raise ValueError(
|
|
"Features in dataset can only be integers or floating point number"
|
|
)
|
|
if enable_categorical:
|
|
raise ValueError("categorical data in arrow is not supported yet.")
|
|
|
|
batches = data.to_batches()
|
|
rb_iter = iter(batches)
|
|
it = record_batch_data_iter(rb_iter)
|
|
next_callback = ctypes.CFUNCTYPE(ctypes.c_int, ctypes.c_void_p)(it)
|
|
handle = ctypes.c_void_p()
|
|
config = from_pystr_to_cstr(
|
|
json.dumps({"missing": missing, "nthread": nthread, "nbatch": len(batches)})
|
|
)
|
|
_check_call(
|
|
_LIB.XGDMatrixCreateFromArrowCallback(
|
|
next_callback,
|
|
config,
|
|
ctypes.byref(handle),
|
|
)
|
|
)
|
|
return handle, feature_names, feature_types
|
|
|
|
|
|
def _is_cudf_df(data: DataType) -> bool:
|
|
return lazy_isinstance(data, "cudf.core.dataframe", "DataFrame")
|
|
|
|
|
|
def _cudf_array_interfaces(data: DataType, cat_codes: list) -> bytes:
|
|
"""Extract CuDF __cuda_array_interface__. This is special as it returns a new list
|
|
of data and a list of array interfaces. The data is list of categorical codes that
|
|
caller can safely ignore, but have to keep their reference alive until usage of
|
|
array interface is finished.
|
|
|
|
"""
|
|
try:
|
|
from cudf.api.types import is_categorical_dtype
|
|
except ImportError:
|
|
from cudf.utils.dtypes import is_categorical_dtype
|
|
|
|
interfaces = []
|
|
|
|
def append(interface: dict) -> None:
|
|
if "mask" in interface:
|
|
interface["mask"] = interface["mask"].__cuda_array_interface__
|
|
interfaces.append(interface)
|
|
|
|
if _is_cudf_ser(data):
|
|
if is_categorical_dtype(data.dtype):
|
|
interface = cat_codes[0].__cuda_array_interface__
|
|
else:
|
|
interface = data.__cuda_array_interface__
|
|
append(interface)
|
|
else:
|
|
for i, col in enumerate(data):
|
|
if is_categorical_dtype(data[col].dtype):
|
|
codes = cat_codes[i]
|
|
interface = codes.__cuda_array_interface__
|
|
else:
|
|
interface = data[col].__cuda_array_interface__
|
|
append(interface)
|
|
interfaces_str = from_pystr_to_cstr(json.dumps(interfaces))
|
|
return interfaces_str
|
|
|
|
|
|
def _transform_cudf_df(
|
|
data: DataType,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
enable_categorical: bool,
|
|
) -> Tuple[ctypes.c_void_p, list, Optional[FeatureNames], Optional[FeatureTypes]]:
|
|
try:
|
|
from cudf.api.types import is_categorical_dtype
|
|
except ImportError:
|
|
from cudf.utils.dtypes import is_categorical_dtype
|
|
|
|
if _is_cudf_ser(data):
|
|
dtypes = [data.dtype]
|
|
else:
|
|
dtypes = data.dtypes
|
|
|
|
if not all(
|
|
dtype.name in _pandas_dtype_mapper
|
|
or (is_categorical_dtype(dtype) and enable_categorical)
|
|
for dtype in dtypes
|
|
):
|
|
_invalid_dataframe_dtype(data)
|
|
|
|
# handle feature names
|
|
if feature_names is None:
|
|
if _is_cudf_ser(data):
|
|
feature_names = [data.name]
|
|
elif lazy_isinstance(data.columns, "cudf.core.multiindex", "MultiIndex"):
|
|
feature_names = [" ".join([str(x) for x in i]) for i in data.columns]
|
|
elif (
|
|
lazy_isinstance(data.columns, "cudf.core.index", "RangeIndex")
|
|
or lazy_isinstance(data.columns, "cudf.core.index", "Int64Index")
|
|
# Unique to cuDF, no equivalence in pandas 1.3.3
|
|
or lazy_isinstance(data.columns, "cudf.core.index", "Int32Index")
|
|
):
|
|
feature_names = list(map(str, data.columns))
|
|
else:
|
|
feature_names = data.columns.format()
|
|
|
|
# handle feature types
|
|
if feature_types is None:
|
|
feature_types = []
|
|
for dtype in dtypes:
|
|
if is_categorical_dtype(dtype) and enable_categorical:
|
|
feature_types.append(CAT_T)
|
|
else:
|
|
feature_types.append(_pandas_dtype_mapper[dtype.name])
|
|
|
|
# handle categorical data
|
|
cat_codes = []
|
|
if _is_cudf_ser(data):
|
|
# unlike pandas, cuDF uses NA for missing data.
|
|
if is_categorical_dtype(data.dtype) and enable_categorical:
|
|
codes = data.cat.codes
|
|
cat_codes.append(codes)
|
|
else:
|
|
for col in data:
|
|
dtype = data[col].dtype
|
|
if is_categorical_dtype(dtype) and enable_categorical:
|
|
codes = data[col].cat.codes
|
|
cat_codes.append(codes)
|
|
elif is_categorical_dtype(dtype):
|
|
raise ValueError(_ENABLE_CAT_ERR)
|
|
else:
|
|
cat_codes.append([])
|
|
|
|
return data, cat_codes, feature_names, feature_types
|
|
|
|
|
|
def _from_cudf_df(
|
|
data: DataType,
|
|
missing: FloatCompatible,
|
|
nthread: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
enable_categorical: bool,
|
|
) -> DispatchedDataBackendReturnType:
|
|
data, cat_codes, feature_names, feature_types = _transform_cudf_df(
|
|
data, feature_names, feature_types, enable_categorical
|
|
)
|
|
interfaces_str = _cudf_array_interfaces(data, cat_codes)
|
|
handle = ctypes.c_void_p()
|
|
config = bytes(json.dumps({"missing": missing, "nthread": nthread}), "utf-8")
|
|
_check_call(
|
|
_LIB.XGDMatrixCreateFromCudaColumnar(
|
|
interfaces_str,
|
|
config,
|
|
ctypes.byref(handle),
|
|
)
|
|
)
|
|
return handle, feature_names, feature_types
|
|
|
|
|
|
def _is_cudf_ser(data: DataType) -> bool:
|
|
return lazy_isinstance(data, "cudf.core.series", "Series")
|
|
|
|
|
|
def _is_cupy_array(data: DataType) -> bool:
|
|
return any(
|
|
lazy_isinstance(data, n, "ndarray")
|
|
for n in ("cupy.core.core", "cupy", "cupy._core.core")
|
|
)
|
|
|
|
|
|
def _transform_cupy_array(data: DataType) -> CupyT:
|
|
import cupy # pylint: disable=import-error
|
|
|
|
if not hasattr(data, "__cuda_array_interface__") and hasattr(data, "__array__"):
|
|
data = cupy.array(data, copy=False)
|
|
if data.dtype.hasobject or data.dtype in [cupy.float16, cupy.bool_]:
|
|
data = data.astype(cupy.float32, copy=False)
|
|
return data
|
|
|
|
|
|
def _from_cupy_array(
|
|
data: DataType,
|
|
missing: FloatCompatible,
|
|
nthread: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
) -> DispatchedDataBackendReturnType:
|
|
"""Initialize DMatrix from cupy ndarray."""
|
|
data = _transform_cupy_array(data)
|
|
interface_str = _cuda_array_interface(data)
|
|
handle = ctypes.c_void_p()
|
|
config = bytes(json.dumps({"missing": missing, "nthread": nthread}), "utf-8")
|
|
_check_call(
|
|
_LIB.XGDMatrixCreateFromCudaArrayInterface(
|
|
interface_str, config, ctypes.byref(handle)
|
|
)
|
|
)
|
|
return handle, feature_names, feature_types
|
|
|
|
|
|
def _is_cupy_csr(data: DataType) -> bool:
|
|
try:
|
|
import cupyx
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, cupyx.scipy.sparse.csr_matrix)
|
|
|
|
|
|
def _is_cupy_csc(data: DataType) -> bool:
|
|
try:
|
|
import cupyx
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, cupyx.scipy.sparse.csc_matrix)
|
|
|
|
|
|
def _is_dlpack(data: DataType) -> bool:
|
|
return "PyCapsule" in str(type(data)) and "dltensor" in str(data)
|
|
|
|
|
|
def _transform_dlpack(data: DataType) -> bool:
|
|
from cupy import fromDlpack # pylint: disable=E0401
|
|
|
|
assert "used_dltensor" not in str(data)
|
|
data = fromDlpack(data)
|
|
return data
|
|
|
|
|
|
def _from_dlpack(
|
|
data: DataType,
|
|
missing: FloatCompatible,
|
|
nthread: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
) -> DispatchedDataBackendReturnType:
|
|
data = _transform_dlpack(data)
|
|
return _from_cupy_array(data, missing, nthread, feature_names, feature_types)
|
|
|
|
|
|
def _is_uri(data: DataType) -> bool:
|
|
return isinstance(data, (str, os.PathLike))
|
|
|
|
|
|
def _from_uri(
|
|
data: DataType,
|
|
missing: Optional[FloatCompatible],
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
data_split_mode: DataSplitMode = DataSplitMode.ROW,
|
|
) -> DispatchedDataBackendReturnType:
|
|
_warn_unused_missing(data, missing)
|
|
handle = ctypes.c_void_p()
|
|
data = os.fspath(os.path.expanduser(data))
|
|
args = {
|
|
"uri": str(data),
|
|
"data_split_mode": int(data_split_mode),
|
|
}
|
|
config = bytes(json.dumps(args), "utf-8")
|
|
_check_call(_LIB.XGDMatrixCreateFromURI(config, ctypes.byref(handle)))
|
|
return handle, feature_names, feature_types
|
|
|
|
|
|
def _is_list(data: DataType) -> bool:
|
|
return isinstance(data, list)
|
|
|
|
|
|
def _from_list(
|
|
data: Sequence,
|
|
missing: FloatCompatible,
|
|
n_threads: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
) -> DispatchedDataBackendReturnType:
|
|
array = np.array(data)
|
|
_check_data_shape(data)
|
|
return _from_numpy_array(array, missing, n_threads, feature_names, feature_types)
|
|
|
|
|
|
def _is_tuple(data: DataType) -> bool:
|
|
return isinstance(data, tuple)
|
|
|
|
|
|
def _from_tuple(
|
|
data: Sequence,
|
|
missing: FloatCompatible,
|
|
n_threads: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
) -> DispatchedDataBackendReturnType:
|
|
return _from_list(data, missing, n_threads, feature_names, feature_types)
|
|
|
|
|
|
def _is_iter(data: DataType) -> bool:
|
|
return isinstance(data, DataIter)
|
|
|
|
|
|
def _has_array_protocol(data: DataType) -> bool:
|
|
return hasattr(data, "__array__")
|
|
|
|
|
|
def _convert_unknown_data(data: DataType) -> DataType:
|
|
warnings.warn(
|
|
f"Unknown data type: {type(data)}, trying to convert it to csr_matrix",
|
|
UserWarning,
|
|
)
|
|
try:
|
|
import scipy.sparse
|
|
except ImportError:
|
|
return None
|
|
|
|
try:
|
|
data = scipy.sparse.csr_matrix(data)
|
|
except Exception: # pylint: disable=broad-except
|
|
return None
|
|
|
|
return data
|
|
|
|
|
|
def dispatch_data_backend(
|
|
data: DataType,
|
|
missing: FloatCompatible, # Or Optional[Float]
|
|
threads: int,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
enable_categorical: bool = False,
|
|
data_split_mode: DataSplitMode = DataSplitMode.ROW,
|
|
) -> DispatchedDataBackendReturnType:
|
|
"""Dispatch data for DMatrix."""
|
|
if not _is_cudf_ser(data) and not _is_pandas_series(data):
|
|
_check_data_shape(data)
|
|
if _is_scipy_csr(data):
|
|
return _from_scipy_csr(data, missing, threads, feature_names, feature_types)
|
|
if _is_scipy_csc(data):
|
|
return _from_scipy_csc(data, missing, threads, feature_names, feature_types)
|
|
if _is_scipy_coo(data):
|
|
return _from_scipy_csr(
|
|
data.tocsr(), missing, threads, feature_names, feature_types
|
|
)
|
|
if _is_numpy_array(data):
|
|
return _from_numpy_array(data, missing, threads, feature_names, feature_types)
|
|
if _is_uri(data):
|
|
return _from_uri(data, missing, feature_names, feature_types, data_split_mode)
|
|
if _is_list(data):
|
|
return _from_list(data, missing, threads, feature_names, feature_types)
|
|
if _is_tuple(data):
|
|
return _from_tuple(data, missing, threads, feature_names, feature_types)
|
|
if _is_pandas_series(data):
|
|
import pandas as pd
|
|
|
|
data = pd.DataFrame(data)
|
|
if _is_pandas_df(data):
|
|
return _from_pandas_df(
|
|
data, enable_categorical, missing, threads, feature_names, feature_types
|
|
)
|
|
if _is_cudf_df(data) or _is_cudf_ser(data):
|
|
return _from_cudf_df(
|
|
data, missing, threads, feature_names, feature_types, enable_categorical
|
|
)
|
|
if _is_cupy_array(data):
|
|
return _from_cupy_array(data, missing, threads, feature_names, feature_types)
|
|
if _is_cupy_csr(data):
|
|
raise TypeError("cupyx CSR is not supported yet.")
|
|
if _is_cupy_csc(data):
|
|
raise TypeError("cupyx CSC is not supported yet.")
|
|
if _is_dlpack(data):
|
|
return _from_dlpack(data, missing, threads, feature_names, feature_types)
|
|
if _is_dt_df(data):
|
|
_warn_unused_missing(data, missing)
|
|
return _from_dt_df(
|
|
data, missing, threads, feature_names, feature_types, enable_categorical
|
|
)
|
|
if _is_modin_df(data):
|
|
return _from_pandas_df(
|
|
data, enable_categorical, missing, threads, feature_names, feature_types
|
|
)
|
|
if _is_modin_series(data):
|
|
return _from_pandas_series(
|
|
data, missing, threads, enable_categorical, feature_names, feature_types
|
|
)
|
|
if _is_arrow(data):
|
|
return _from_arrow(
|
|
data, missing, threads, feature_names, feature_types, enable_categorical
|
|
)
|
|
if _has_array_protocol(data):
|
|
array = np.asarray(data)
|
|
return _from_numpy_array(array, missing, threads, feature_names, feature_types)
|
|
|
|
converted = _convert_unknown_data(data)
|
|
if converted is not None:
|
|
return _from_scipy_csr(
|
|
converted, missing, threads, feature_names, feature_types
|
|
)
|
|
|
|
raise TypeError("Not supported type for data." + str(type(data)))
|
|
|
|
|
|
def _validate_meta_shape(data: DataType, name: str) -> None:
|
|
if hasattr(data, "shape"):
|
|
msg = f"Invalid shape: {data.shape} for {name}"
|
|
if name in _matrix_meta:
|
|
if len(data.shape) > 2:
|
|
raise ValueError(msg)
|
|
return
|
|
|
|
if len(data.shape) > 2 or (
|
|
len(data.shape) == 2 and (data.shape[1] != 0 and data.shape[1] != 1)
|
|
):
|
|
raise ValueError(f"Invalid shape: {data.shape} for {name}")
|
|
|
|
|
|
def _meta_from_numpy(
|
|
data: np.ndarray,
|
|
field: str,
|
|
dtype: Optional[NumpyDType],
|
|
handle: ctypes.c_void_p,
|
|
) -> None:
|
|
data, dtype = _ensure_np_dtype(data, dtype)
|
|
interface = data.__array_interface__
|
|
if interface.get("mask", None) is not None:
|
|
raise ValueError("Masked array is not supported.")
|
|
interface_str = _array_interface(data)
|
|
_check_call(_LIB.XGDMatrixSetInfoFromInterface(handle, c_str(field), interface_str))
|
|
|
|
|
|
def _meta_from_list(
|
|
data: Sequence, field: str, dtype: Optional[NumpyDType], handle: ctypes.c_void_p
|
|
) -> None:
|
|
data_np = np.array(data)
|
|
_meta_from_numpy(data_np, field, dtype, handle)
|
|
|
|
|
|
def _meta_from_tuple(
|
|
data: Sequence, field: str, dtype: Optional[NumpyDType], handle: ctypes.c_void_p
|
|
) -> None:
|
|
return _meta_from_list(data, field, dtype, handle)
|
|
|
|
|
|
def _meta_from_cudf_df(data: DataType, field: str, handle: ctypes.c_void_p) -> None:
|
|
if field not in _matrix_meta:
|
|
_meta_from_cudf_series(data.iloc[:, 0], field, handle)
|
|
else:
|
|
data = data.values
|
|
interface = _cuda_array_interface(data)
|
|
_check_call(_LIB.XGDMatrixSetInfoFromInterface(handle, c_str(field), interface))
|
|
|
|
|
|
def _meta_from_cudf_series(data: DataType, field: str, handle: ctypes.c_void_p) -> None:
|
|
interface = bytes(json.dumps([data.__cuda_array_interface__], indent=2), "utf-8")
|
|
_check_call(_LIB.XGDMatrixSetInfoFromInterface(handle, c_str(field), interface))
|
|
|
|
|
|
def _meta_from_cupy_array(data: DataType, field: str, handle: ctypes.c_void_p) -> None:
|
|
data = _transform_cupy_array(data)
|
|
interface = bytes(json.dumps([data.__cuda_array_interface__], indent=2), "utf-8")
|
|
_check_call(_LIB.XGDMatrixSetInfoFromInterface(handle, c_str(field), interface))
|
|
|
|
|
|
def _meta_from_dt(
|
|
data: DataType, field: str, dtype: Optional[NumpyDType], handle: ctypes.c_void_p
|
|
) -> None:
|
|
data, _, _ = _transform_dt_df(data, None, None, field, dtype)
|
|
_meta_from_numpy(data, field, dtype, handle)
|
|
|
|
|
|
def dispatch_meta_backend(
|
|
matrix: DMatrix, data: DataType, name: str, dtype: Optional[NumpyDType] = None
|
|
) -> None:
|
|
"""Dispatch for meta info."""
|
|
handle = matrix.handle
|
|
assert handle is not None
|
|
_validate_meta_shape(data, name)
|
|
if data is None:
|
|
return
|
|
if _is_list(data):
|
|
_meta_from_list(data, name, dtype, handle)
|
|
return
|
|
if _is_tuple(data):
|
|
_meta_from_tuple(data, name, dtype, handle)
|
|
return
|
|
if _is_numpy_array(data):
|
|
_meta_from_numpy(data, name, dtype, handle)
|
|
return
|
|
if _is_pandas_df(data):
|
|
data, _, _ = _transform_pandas_df(data, False, meta=name, meta_type=dtype)
|
|
_meta_from_numpy(data, name, dtype, handle)
|
|
return
|
|
if _is_pandas_series(data):
|
|
_meta_from_pandas_series(data, name, dtype, handle)
|
|
return
|
|
if _is_dlpack(data):
|
|
data = _transform_dlpack(data)
|
|
_meta_from_cupy_array(data, name, handle)
|
|
return
|
|
if _is_cupy_array(data):
|
|
_meta_from_cupy_array(data, name, handle)
|
|
return
|
|
if _is_cudf_ser(data):
|
|
_meta_from_cudf_series(data, name, handle)
|
|
return
|
|
if _is_cudf_df(data):
|
|
_meta_from_cudf_df(data, name, handle)
|
|
return
|
|
if _is_dt_df(data):
|
|
_meta_from_dt(data, name, dtype, handle)
|
|
return
|
|
if _is_modin_df(data):
|
|
data, _, _ = _transform_pandas_df(data, False, meta=name, meta_type=dtype)
|
|
_meta_from_numpy(data, name, dtype, handle)
|
|
return
|
|
if _is_modin_series(data):
|
|
data = data.values.astype("float")
|
|
assert len(data.shape) == 1 or data.shape[1] == 0 or data.shape[1] == 1
|
|
_meta_from_numpy(data, name, dtype, handle)
|
|
return
|
|
if _has_array_protocol(data):
|
|
# pyarrow goes here.
|
|
array = np.asarray(data)
|
|
_meta_from_numpy(array, name, dtype, handle)
|
|
return
|
|
raise TypeError("Unsupported type for " + name, str(type(data)))
|
|
|
|
|
|
class SingleBatchInternalIter(DataIter): # pylint: disable=R0902
|
|
"""An iterator for single batch data to help creating device DMatrix.
|
|
Transforming input directly to histogram with normal single batch data API
|
|
can not access weight for sketching. So this iterator acts as a staging
|
|
area for meta info.
|
|
|
|
"""
|
|
|
|
def __init__(self, **kwargs: Any) -> None:
|
|
self.kwargs = kwargs
|
|
self.it = 0 # pylint: disable=invalid-name
|
|
|
|
# This does not necessarily increase memory usage as the data transformation
|
|
# might use memory.
|
|
super().__init__(release_data=False)
|
|
|
|
def next(self, input_data: Callable) -> int:
|
|
if self.it == 1:
|
|
return 0
|
|
self.it += 1
|
|
input_data(**self.kwargs)
|
|
return 1
|
|
|
|
def reset(self) -> None:
|
|
self.it = 0
|
|
|
|
|
|
def _proxy_transform(
|
|
data: DataType,
|
|
feature_names: Optional[FeatureNames],
|
|
feature_types: Optional[FeatureTypes],
|
|
enable_categorical: bool,
|
|
) -> Tuple[
|
|
Union[bool, ctypes.c_void_p, np.ndarray],
|
|
Optional[list],
|
|
Optional[FeatureNames],
|
|
Optional[FeatureTypes],
|
|
]:
|
|
if _is_cudf_df(data) or _is_cudf_ser(data):
|
|
return _transform_cudf_df(
|
|
data, feature_names, feature_types, enable_categorical
|
|
)
|
|
if _is_cupy_array(data):
|
|
data = _transform_cupy_array(data)
|
|
return data, None, feature_names, feature_types
|
|
if _is_dlpack(data):
|
|
return _transform_dlpack(data), None, feature_names, feature_types
|
|
if _is_list(data) or _is_tuple(data):
|
|
data = np.array(data)
|
|
if _is_numpy_array(data):
|
|
data, _ = _ensure_np_dtype(data, data.dtype)
|
|
return data, None, feature_names, feature_types
|
|
if _is_scipy_csr(data):
|
|
data = transform_scipy_sparse(data, True)
|
|
return data, None, feature_names, feature_types
|
|
if _is_pandas_series(data):
|
|
import pandas as pd
|
|
|
|
data = pd.DataFrame(data)
|
|
if _is_pandas_df(data):
|
|
arr, feature_names, feature_types = _transform_pandas_df(
|
|
data, enable_categorical, feature_names, feature_types
|
|
)
|
|
arr, _ = _ensure_np_dtype(arr, arr.dtype)
|
|
return arr, None, feature_names, feature_types
|
|
raise TypeError("Value type is not supported for data iterator:" + str(type(data)))
|
|
|
|
|
|
def dispatch_proxy_set_data(
|
|
proxy: _ProxyDMatrix,
|
|
data: DataType,
|
|
cat_codes: Optional[list],
|
|
allow_host: bool,
|
|
) -> None:
|
|
"""Dispatch for QuantileDMatrix."""
|
|
if not _is_cudf_ser(data) and not _is_pandas_series(data):
|
|
_check_data_shape(data)
|
|
|
|
if _is_cudf_df(data):
|
|
# pylint: disable=W0212
|
|
proxy._set_data_from_cuda_columnar(data, cast(List, cat_codes))
|
|
return
|
|
if _is_cudf_ser(data):
|
|
# pylint: disable=W0212
|
|
proxy._set_data_from_cuda_columnar(data, cast(List, cat_codes))
|
|
return
|
|
if _is_cupy_array(data):
|
|
proxy._set_data_from_cuda_interface(data) # pylint: disable=W0212
|
|
return
|
|
if _is_dlpack(data):
|
|
data = _transform_dlpack(data)
|
|
proxy._set_data_from_cuda_interface(data) # pylint: disable=W0212
|
|
return
|
|
|
|
err = TypeError("Value type is not supported for data iterator:" + str(type(data)))
|
|
|
|
if not allow_host:
|
|
raise err
|
|
|
|
if _is_numpy_array(data):
|
|
_check_data_shape(data)
|
|
proxy._set_data_from_array(data) # pylint: disable=W0212
|
|
return
|
|
if _is_scipy_csr(data):
|
|
proxy._set_data_from_csr(data) # pylint: disable=W0212
|
|
return
|
|
raise err
|