c1ca872d1e
* Modin DF support * mode change * tests were added, ci env was extended * mode change * Remove redundant installation of modin * Add a pytest skip marker for modin * Install Modin[ray] from PyPI * fix interfering * avoid extra conversion * delete cv test for modin * revert cv function Co-authored-by: ShvetsKS <kirill.shvets@intel.com> Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
802 lines
27 KiB
Python
802 lines
27 KiB
Python
# pylint: disable=too-many-arguments, too-many-branches
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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 warnings
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import numpy as np
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from .core import c_array, _LIB, _check_call, c_str
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from .core import DataIter, DeviceQuantileDMatrix, DMatrix
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from .compat import lazy_isinstance, os_fspath, os_PathLike
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c_bst_ulong = ctypes.c_uint64 # pylint: disable=invalid-name
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def _warn_unused_missing(data, missing):
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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:' +
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str(type(data)), UserWarning)
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def _check_complex(data):
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'''Test whether data is complex using `dtype` attribute.'''
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complex_dtypes = (np.complex128, np.complex64,
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np.cfloat, np.cdouble, np.clongdouble)
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if hasattr(data, 'dtype') and data.dtype in complex_dtypes:
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raise ValueError('Complex data not supported')
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def _is_scipy_csr(data):
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try:
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import scipy
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except ImportError:
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scipy = None
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return False
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return isinstance(data, scipy.sparse.csr_matrix)
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def _from_scipy_csr(data, missing, feature_names, feature_types):
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'''Initialize data from a CSR matrix.'''
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if len(data.indices) != len(data.data):
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raise ValueError('length mismatch: {} vs {}'.format(
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len(data.indices), len(data.data)))
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_warn_unused_missing(data, missing)
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handle = ctypes.c_void_p()
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_check_call(_LIB.XGDMatrixCreateFromCSREx(
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c_array(ctypes.c_size_t, data.indptr),
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c_array(ctypes.c_uint, data.indices),
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c_array(ctypes.c_float, data.data),
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ctypes.c_size_t(len(data.indptr)),
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ctypes.c_size_t(len(data.data)),
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ctypes.c_size_t(data.shape[1]),
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ctypes.byref(handle)))
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return handle, feature_names, feature_types
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def _is_scipy_csc(data):
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try:
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import scipy
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except ImportError:
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scipy = None
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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(data, missing, feature_names, feature_types):
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if len(data.indices) != len(data.data):
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raise ValueError('length mismatch: {} vs {}'.format(
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len(data.indices), len(data.data)))
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_warn_unused_missing(data, missing)
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handle = ctypes.c_void_p()
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_check_call(_LIB.XGDMatrixCreateFromCSCEx(
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c_array(ctypes.c_size_t, data.indptr),
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c_array(ctypes.c_uint, data.indices),
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c_array(ctypes.c_float, data.data),
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ctypes.c_size_t(len(data.indptr)),
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ctypes.c_size_t(len(data.data)),
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ctypes.c_size_t(data.shape[0]),
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ctypes.byref(handle)))
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return handle, feature_names, feature_types
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def _is_numpy_array(data):
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return isinstance(data, (np.ndarray, np.matrix))
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def _maybe_np_slice(data, dtype):
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'''Handle numpy slice. This can be removed if we use __array_interface__.
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'''
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try:
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if not data.flags.c_contiguous:
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warnings.warn(
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"Use subset (sliced data) of np.ndarray is not recommended " +
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"because it will generate extra copies and increase " +
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"memory consumption")
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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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return data
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def _transform_np_array(data: np.ndarray):
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if not isinstance(data, np.ndarray) and hasattr(data, '__array__'):
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data = np.array(data, copy=False)
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if len(data.shape) != 2:
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raise ValueError('Expecting 2 dimensional numpy.ndarray, got: ',
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data.shape)
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# flatten the array by rows and ensure it is float32. we try to avoid
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# data copies if possible (reshape returns a view when possible and we
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# explicitly tell np.array to try and avoid copying)
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flatten = np.array(data.reshape(data.size), copy=False,
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dtype=np.float32)
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flatten = _maybe_np_slice(flatten, np.float32)
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_check_complex(data)
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return flatten
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def _from_numpy_array(data, missing, nthread, feature_names, feature_types):
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"""Initialize data from a 2-D numpy matrix.
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If ``mat`` does not have ``order='C'`` (aka row-major) or is
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not contiguous, a temporary copy will be made.
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If ``mat`` does not have ``dtype=numpy.float32``, a temporary copy will
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be made.
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So there could be as many as two temporary data copies; be mindful of
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input layout and type if memory use is a concern.
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"""
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flatten = _transform_np_array(data)
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handle = ctypes.c_void_p()
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_check_call(_LIB.XGDMatrixCreateFromMat_omp(
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flatten.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
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c_bst_ulong(data.shape[0]),
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c_bst_ulong(data.shape[1]),
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ctypes.c_float(missing),
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ctypes.byref(handle),
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ctypes.c_int(nthread)))
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return handle, feature_names, feature_types
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def _is_pandas_df(data):
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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):
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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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def _transform_pandas_df(data, feature_names=None, feature_types=None,
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meta=None, meta_type=None):
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from pandas import MultiIndex, Int64Index
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from pandas.api.types import is_sparse
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data_dtypes = data.dtypes
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if not all(dtype.name in _pandas_dtype_mapper or is_sparse(dtype)
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for dtype in data_dtypes):
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bad_fields = [
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str(data.columns[i]) 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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msg = """DataFrame.dtypes for data must be int, float or bool.
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Did not expect the data types in fields """
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raise ValueError(msg + ', '.join(bad_fields))
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if feature_names is None and meta is None:
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if isinstance(data.columns, MultiIndex):
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feature_names = [
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' '.join([str(x) for x in i]) for i in data.columns
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]
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elif isinstance(data.columns, Int64Index):
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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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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[
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dtype.subtype.name])
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else:
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feature_types.append(_pandas_dtype_mapper[dtype.name])
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if meta and len(data.columns) > 1:
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raise ValueError(
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'DataFrame for {meta} cannot have multiple columns'.format(
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meta=meta))
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dtype = meta_type if meta_type else np.float32
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data = np.ascontiguousarray(data.values, dtype=dtype)
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return data, feature_names, feature_types
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def _from_pandas_df(data, missing, nthread, feature_names, feature_types):
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data, feature_names, feature_types = _transform_pandas_df(
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data, feature_names, feature_types)
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return _from_numpy_array(data, missing, nthread, feature_names,
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feature_types)
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def _is_pandas_series(data):
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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.Series)
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def _is_modin_series(data):
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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.Series)
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def _from_pandas_series(data, missing, nthread, feature_types, feature_names):
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return _from_numpy_array(data.values.astype('float'), missing, nthread,
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feature_names, feature_types)
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def _is_dt_df(data):
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return lazy_isinstance(data, 'datatable', 'Frame') or \
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lazy_isinstance(data, 'datatable', 'DataTable')
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_dt_type_mapper = {'bool': 'bool', 'int': 'int', 'real': 'float'}
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_dt_type_mapper2 = {'bool': 'i', 'int': 'int', 'real': 'float'}
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def _transform_dt_df(data, feature_names, feature_types, meta=None,
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meta_type=None):
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"""Validate feature names and types if data table"""
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if meta and data.shape[1] > 1:
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raise ValueError(
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'DataTable for label or weight cannot have multiple columns')
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if meta:
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# below requires new dt version
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# extract first column
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data = data.to_numpy()[:, 0].astype(meta_type)
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return data, None, None
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data_types_names = tuple(lt.name for lt in data.ltypes)
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bad_fields = [data.names[i]
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for i, type_name in enumerate(data_types_names)
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if type_name not in _dt_type_mapper]
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if bad_fields:
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msg = """DataFrame.types for data must be int, float or bool.
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Did not expect the data types in fields """
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raise ValueError(msg + ', '.join(bad_fields))
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if feature_names is None and meta is None:
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feature_names = data.names
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# always return stypes for dt ingestion
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if feature_types is not None:
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raise ValueError(
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'DataTable has own feature types, cannot pass them in.')
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feature_types = np.vectorize(_dt_type_mapper2.get)(
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data_types_names).tolist()
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return data, feature_names, feature_types
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def _from_dt_df(data, missing, nthread, feature_names, feature_types):
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data, feature_names, feature_types = _transform_dt_df(
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data, feature_names, feature_types, None, None)
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ptrs = (ctypes.c_void_p * data.ncols)()
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if hasattr(data, "internal") and hasattr(data.internal, "column"):
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# datatable>0.8.0
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for icol in range(data.ncols):
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col = data.internal.column(icol)
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ptr = col.data_pointer
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ptrs[icol] = ctypes.c_void_p(ptr)
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else:
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# datatable<=0.8.0
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from datatable.internal import \
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frame_column_data_r # pylint: disable=no-name-in-module
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for icol in range(data.ncols):
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ptrs[icol] = frame_column_data_r(data, icol)
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# always return stypes for dt ingestion
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feature_type_strings = (ctypes.c_char_p * data.ncols)()
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for icol in range(data.ncols):
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feature_type_strings[icol] = ctypes.c_char_p(
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data.stypes[icol].name.encode('utf-8'))
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_warn_unused_missing(data, missing)
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handle = ctypes.c_void_p()
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_check_call(_LIB.XGDMatrixCreateFromDT(
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ptrs, feature_type_strings,
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c_bst_ulong(data.shape[0]),
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c_bst_ulong(data.shape[1]),
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ctypes.byref(handle),
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ctypes.c_int(nthread)))
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return handle, feature_names, feature_types
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def _is_cudf_df(data):
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try:
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import cudf
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except ImportError:
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return False
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return hasattr(cudf, 'DataFrame') and isinstance(data, cudf.DataFrame)
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def _cudf_array_interfaces(data):
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'''Extract CuDF __cuda_array_interface__'''
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interfaces = []
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if _is_cudf_ser(data):
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interfaces.append(data.__cuda_array_interface__)
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else:
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for col in data:
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interface = data[col].__cuda_array_interface__
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if 'mask' in interface:
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interface['mask'] = interface['mask'].__cuda_array_interface__
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interfaces.append(interface)
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interfaces_str = bytes(json.dumps(interfaces, indent=2), 'utf-8')
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return interfaces_str
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def _transform_cudf_df(data, feature_names, feature_types):
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if feature_names is None:
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if _is_cudf_ser(data):
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feature_names = [data.name]
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elif lazy_isinstance(
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data.columns, 'cudf.core.multiindex', 'MultiIndex'):
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feature_names = [
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' '.join([str(x) for x in i])
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for i in data.columns
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]
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else:
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feature_names = data.columns.format()
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if feature_types is None:
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if _is_cudf_ser(data):
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dtypes = [data.dtype]
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else:
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dtypes = data.dtypes
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feature_types = [_pandas_dtype_mapper[d.name]
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for d in dtypes]
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return data, feature_names, feature_types
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def _from_cudf_df(data, missing, nthread, feature_names, feature_types):
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data, feature_names, feature_types = _transform_cudf_df(
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data, feature_names, feature_types)
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interfaces_str = _cudf_array_interfaces(data)
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handle = ctypes.c_void_p()
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_check_call(
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_LIB.XGDMatrixCreateFromArrayInterfaceColumns(
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interfaces_str,
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ctypes.c_float(missing),
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ctypes.c_int(nthread),
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ctypes.byref(handle)))
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return handle, feature_names, feature_types
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def _is_cudf_ser(data):
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try:
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import cudf
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except ImportError:
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return False
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return isinstance(data, cudf.Series)
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def _is_cupy_array(data):
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try:
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import cupy
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except ImportError:
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return False
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return isinstance(data, cupy.ndarray)
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def _transform_cupy_array(data):
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if not hasattr(data, '__cuda_array_interface__') and hasattr(
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data, '__array__'):
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import cupy # pylint: disable=import-error
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data = cupy.array(data, copy=False)
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return data
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def _from_cupy_array(data, missing, nthread, feature_names, feature_types):
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"""Initialize DMatrix from cupy ndarray."""
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data = _transform_cupy_array(data)
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interface = data.__cuda_array_interface__
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if 'mask' in interface:
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interface['mask'] = interface['mask'].__cuda_array_interface__
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interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')
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handle = ctypes.c_void_p()
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_check_call(
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_LIB.XGDMatrixCreateFromArrayInterface(
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interface_str,
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ctypes.c_float(missing),
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ctypes.c_int(nthread),
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ctypes.byref(handle)))
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return handle, feature_names, feature_types
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def _is_cupy_csr(data):
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try:
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import cupyx
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except ImportError:
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return False
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return isinstance(data, cupyx.scipy.sparse.csr_matrix)
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def _is_cupy_csc(data):
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try:
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import cupyx
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except ImportError:
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return False
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return isinstance(data, cupyx.scipy.sparse.csc_matrix)
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def _is_dlpack(data):
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return 'PyCapsule' in str(type(data)) and "dltensor" in str(data)
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def _transform_dlpack(data):
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from cupy import fromDlpack # pylint: disable=E0401
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assert 'used_dltensor' not in str(data)
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data = fromDlpack(data)
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return data
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def _from_dlpack(data, missing, nthread, feature_names, feature_types):
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data = _transform_dlpack(data)
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return _from_cupy_array(data, missing, nthread, feature_names,
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feature_types)
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def _is_uri(data):
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return isinstance(data, (str, os_PathLike))
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def _from_uri(data, missing, feature_names, feature_types):
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_warn_unused_missing(data, missing)
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handle = ctypes.c_void_p()
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_check_call(_LIB.XGDMatrixCreateFromFile(c_str(os_fspath(data)),
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ctypes.c_int(1),
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ctypes.byref(handle)))
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return handle, feature_names, feature_types
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def _is_list(data):
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return isinstance(data, list)
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def _from_list(data, missing, feature_names, feature_types):
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raise TypeError('List input data is not supported for data')
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def _is_tuple(data):
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return isinstance(data, tuple)
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def _from_tuple(data, missing, feature_names, feature_types):
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return _from_list(data, missing, feature_names, feature_types)
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def _is_iter(data):
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return isinstance(data, DataIter)
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|
|
def _has_array_protocol(data):
|
|
return hasattr(data, '__array__')
|
|
|
|
|
|
def dispatch_data_backend(data, missing, threads,
|
|
feature_names, feature_types):
|
|
'''Dispatch data for DMatrix.'''
|
|
if _is_scipy_csr(data):
|
|
return _from_scipy_csr(data, missing, feature_names, feature_types)
|
|
if _is_scipy_csc(data):
|
|
return _from_scipy_csc(data, missing, 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)
|
|
if _is_list(data):
|
|
return _from_list(data, missing, feature_names, feature_types)
|
|
if _is_tuple(data):
|
|
return _from_tuple(data, missing, feature_names, feature_types)
|
|
if _is_pandas_df(data):
|
|
return _from_pandas_df(data, missing, threads,
|
|
feature_names, feature_types)
|
|
if _is_pandas_series(data):
|
|
return _from_pandas_series(data, missing, threads, feature_names,
|
|
feature_types)
|
|
if _is_cudf_df(data):
|
|
return _from_cudf_df(data, missing, threads, feature_names,
|
|
feature_types)
|
|
if _is_cudf_ser(data):
|
|
return _from_cudf_df(data, missing, threads, feature_names,
|
|
feature_types)
|
|
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)
|
|
if _is_modin_df(data):
|
|
return _from_pandas_df(data, missing, threads,
|
|
feature_names, feature_types)
|
|
if _is_modin_series(data):
|
|
return _from_pandas_series(data, missing, threads, feature_names,
|
|
feature_types)
|
|
if _has_array_protocol(data):
|
|
pass
|
|
raise TypeError('Not supported type for data.' + str(type(data)))
|
|
|
|
|
|
def _to_data_type(dtype: str, name: str):
|
|
dtype_map = {'float32': 1, 'float64': 2, 'uint32': 3, 'uint64': 4}
|
|
if dtype not in dtype_map.keys():
|
|
raise TypeError(
|
|
f'Expecting float32, float64, uint32, uint64, got {dtype} ' +
|
|
f'for {name}.')
|
|
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)
|
|
ptr = interface['data'][0]
|
|
ptr = ctypes.c_void_p(ptr)
|
|
_check_call(_LIB.XGDMatrixSetDenseInfo(
|
|
handle,
|
|
c_str(field),
|
|
ptr,
|
|
c_bst_ulong(size),
|
|
c_type
|
|
))
|
|
|
|
|
|
def _meta_from_list(data, field, dtype, handle):
|
|
data = np.array(data)
|
|
_meta_from_numpy(data, field, dtype, handle)
|
|
|
|
|
|
def _meta_from_tuple(data, field, dtype, handle):
|
|
return _meta_from_list(data, field, dtype, handle)
|
|
|
|
|
|
def _meta_from_cudf_df(data, field, handle):
|
|
if len(data.columns) != 1:
|
|
raise ValueError(
|
|
'Expecting meta-info to contain a single column')
|
|
data = data[data.columns[0]]
|
|
|
|
interface = bytes(json.dumps([data.__cuda_array_interface__],
|
|
indent=2), 'utf-8')
|
|
_check_call(_LIB.XGDMatrixSetInfoFromInterface(handle,
|
|
c_str(field),
|
|
interface))
|
|
|
|
|
|
def _meta_from_cudf_series(data, field, handle):
|
|
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, field, handle):
|
|
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, field, dtype, handle):
|
|
data, _, _ = _transform_dt_df(data, None, None)
|
|
_meta_from_numpy(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):
|
|
_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, meta=name, meta_type=dtype)
|
|
_meta_from_numpy(data, name, dtype, handle)
|
|
return
|
|
if _is_pandas_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 _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, 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):
|
|
pass
|
|
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, data, label, weight, base_margin, group,
|
|
label_lower_bound, label_upper_bound,
|
|
feature_names, feature_types):
|
|
self.data = data
|
|
self.label = label
|
|
self.weight = weight
|
|
self.base_margin = base_margin
|
|
self.group = group
|
|
self.label_lower_bound = label_lower_bound
|
|
self.label_upper_bound = label_upper_bound
|
|
self.feature_names = feature_names
|
|
self.feature_types = feature_types
|
|
self.it = 0 # pylint: disable=invalid-name
|
|
super().__init__()
|
|
|
|
def next(self, input_data):
|
|
if self.it == 1:
|
|
return 0
|
|
self.it += 1
|
|
input_data(data=self.data, label=self.label,
|
|
weight=self.weight, base_margin=self.base_margin,
|
|
group=self.group,
|
|
label_lower_bound=self.label_lower_bound,
|
|
label_upper_bound=self.label_upper_bound,
|
|
feature_names=self.feature_names,
|
|
feature_types=self.feature_types)
|
|
return 1
|
|
|
|
def reset(self):
|
|
self.it = 0
|
|
|
|
|
|
def init_device_quantile_dmatrix(
|
|
data, missing, max_bin, threads, feature_names, feature_types, **meta):
|
|
'''Constructor for DeviceQuantileDMatrix.'''
|
|
if not any([_is_cudf_df(data), _is_cudf_ser(data), _is_cupy_array(data),
|
|
_is_dlpack(data), _is_iter(data)]):
|
|
raise TypeError(str(type(data)) +
|
|
' is not supported for DeviceQuantileDMatrix')
|
|
if _is_dlpack(data):
|
|
# We specialize for dlpack because cupy will take the memory from it so
|
|
# it can't be transformed twice.
|
|
data = _transform_dlpack(data)
|
|
if _is_iter(data):
|
|
it = data
|
|
else:
|
|
it = SingleBatchInternalIter(
|
|
data, **meta, feature_names=feature_names,
|
|
feature_types=feature_types)
|
|
|
|
reset_factory = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
|
|
reset_callback = reset_factory(it.reset_wrapper)
|
|
next_factory = ctypes.CFUNCTYPE(
|
|
ctypes.c_int,
|
|
ctypes.c_void_p,
|
|
)
|
|
next_callback = next_factory(it.next_wrapper)
|
|
handle = ctypes.c_void_p()
|
|
ret = _LIB.XGDeviceQuantileDMatrixCreateFromCallback(
|
|
None,
|
|
it.proxy.handle,
|
|
reset_callback,
|
|
next_callback,
|
|
ctypes.c_float(missing),
|
|
ctypes.c_int(threads),
|
|
ctypes.c_int(max_bin),
|
|
ctypes.byref(handle)
|
|
)
|
|
if it.exception:
|
|
raise it.exception
|
|
# delay check_call to throw intermediate exception first
|
|
_check_call(ret)
|
|
matrix = DeviceQuantileDMatrix(handle)
|
|
feature_names = matrix.feature_names
|
|
feature_types = matrix.feature_types
|
|
matrix.handle = None
|
|
return handle, feature_names, feature_types
|
|
|
|
|
|
def _device_quantile_transform(data, feature_names, feature_types):
|
|
if _is_cudf_df(data):
|
|
return _transform_cudf_df(data, feature_names, feature_types)
|
|
if _is_cudf_ser(data):
|
|
return _transform_cudf_df(data, feature_names, feature_types)
|
|
if _is_cupy_array(data):
|
|
data = _transform_cupy_array(data)
|
|
return data, feature_names, feature_types
|
|
if _is_dlpack(data):
|
|
return _transform_dlpack(data), feature_names, feature_types
|
|
raise TypeError('Value type is not supported for data iterator:' +
|
|
str(type(data)))
|
|
|
|
|
|
def dispatch_device_quantile_dmatrix_set_data(proxy, data):
|
|
'''Dispatch for DeviceQuantileDMatrix.'''
|
|
if _is_cudf_df(data):
|
|
proxy._set_data_from_cuda_columnar(data) # pylint: disable=W0212
|
|
return
|
|
if _is_cudf_ser(data):
|
|
proxy._set_data_from_cuda_columnar(data) # pylint: disable=W0212
|
|
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
|
|
raise TypeError('Value type is not supported for data iterator:' +
|
|
str(type(data)))
|