1054 lines
32 KiB
Python
1054 lines
32 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 warnings
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import os
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from typing import Any, Tuple, Callable, Optional, List, Union
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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 _cuda_array_interface
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from .core import DataIter, _ProxyDMatrix, DMatrix
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from .compat import lazy_isinstance, DataFrame
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c_bst_ulong = ctypes.c_uint64 # pylint: disable=invalid-name
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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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# For now it's base_margin for multi-class, but it can be extended to label once we have
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# multi-output.
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_matrix_meta = {"base_margin", "label"}
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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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|
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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 _check_data_shape(data: Any) -> 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):
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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 _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 _from_scipy_csr(
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data,
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missing,
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nthread,
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feature_names: Optional[List[str]],
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feature_types: Optional[List[str]],
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):
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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(
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f"length mismatch: {len(data.indices)} vs {len(data.data)}"
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)
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handle = ctypes.c_void_p()
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args = {
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"missing": float(missing),
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"nthread": int(nthread),
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}
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config = bytes(json.dumps(args), "utf-8")
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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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ctypes.c_size_t(data.shape[1]),
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config,
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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):
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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(
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data,
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missing,
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feature_names: Optional[List[str]],
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feature_types: Optional[List[str]],
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):
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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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_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_scipy_coo(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.coo_matrix)
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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 _ensure_np_dtype(data, dtype) -> Tuple[np.ndarray, np.dtype]:
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if data.dtype.hasobject or data.dtype in [np.float16, np.bool_]:
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data = data.astype(np.float32, copy=False)
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dtype = np.float32
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return data, dtype
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def _maybe_np_slice(data: np.ndarray, dtype) -> np.ndarray:
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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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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,
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missing,
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nthread,
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feature_names: Optional[List[str]],
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feature_types: Optional[List[str]],
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):
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"""Initialize data from a 2-D numpy matrix.
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"""
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if len(data.shape) != 2:
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raise ValueError(
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"Expecting 2 dimensional numpy.ndarray, got: ", data.shape
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)
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data, _ = _ensure_np_dtype(data, data.dtype)
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handle = ctypes.c_void_p()
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args = {
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"missing": float(missing),
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"nthread": int(nthread),
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}
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config = bytes(json.dumps(args), "utf-8")
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_check_call(
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_LIB.XGDMatrixCreateFromDense(
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_array_interface(data),
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config,
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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):
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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 _invalid_dataframe_dtype(data: Any) -> 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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str(data.columns[i])
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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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msg = """DataFrame.dtypes for data must be int, float, bool or category. When
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categorical type is supplied, DMatrix parameter `enable_categorical` must
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be set to `True`.""" + err
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raise ValueError(msg)
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def _transform_pandas_df(
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data: DataFrame,
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enable_categorical: bool,
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feature_names: Optional[List[str]] = None,
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feature_types: Optional[List[str]] = None,
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meta: Optional[str] = None,
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meta_type: Optional[str] = None,
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) -> Tuple[np.ndarray, Optional[List[str]], Optional[List[str]]]:
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import pandas as pd
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from pandas.api.types import is_sparse, is_categorical_dtype
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if not all(
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dtype.name in _pandas_dtype_mapper
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or is_sparse(dtype)
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or (is_categorical_dtype(dtype) and enable_categorical)
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for dtype in data.dtypes
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):
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_invalid_dataframe_dtype(data)
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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 i, dtype in enumerate(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 is_categorical_dtype(dtype) 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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# handle category codes.
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transformed = pd.DataFrame()
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if enable_categorical:
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for i, dtype in enumerate(data.dtypes):
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if is_categorical_dtype(dtype):
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# pandas uses -1 as default missing value for categorical data
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transformed[data.columns[i]] = (
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data[data.columns[i]]
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.cat.codes.astype(np.float32)
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.replace(-1.0, np.NaN)
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)
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else:
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transformed[data.columns[i]] = data[data.columns[i]]
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else:
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transformed = data
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if meta and len(data.columns) > 1 and meta not in _matrix_meta:
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raise ValueError(f"DataFrame for {meta} cannot have multiple columns")
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dtype = meta_type if meta_type else np.float32
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arr = transformed.values
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if meta_type:
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arr = arr.astype(meta_type)
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return arr, feature_names, feature_types
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|
|
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def _from_pandas_df(
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data: DataFrame,
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enable_categorical: bool,
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missing: float,
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nthread: int,
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feature_names: Optional[List[str]],
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feature_types: Optional[List[str]],
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) -> Tuple[ctypes.c_void_p, Optional[List[str]], Optional[List[str]]]:
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data, feature_names, feature_types = _transform_pandas_df(
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data, enable_categorical, feature_names, feature_types
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)
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return _from_numpy_array(data, missing, nthread, feature_names, feature_types)
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|
|
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|
def _is_pandas_series(data):
|
|
try:
|
|
import pandas as pd
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, pd.Series)
|
|
|
|
|
|
def _meta_from_pandas_series(
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data, name: str, dtype: Optional[str], handle: ctypes.c_void_p
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|
) -> None:
|
|
"""Help transform pandas series for meta data like labels"""
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data = data.values.astype('float')
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from pandas.api.types import is_sparse
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if is_sparse(data):
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data = data.to_dense()
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assert len(data.shape) == 1 or data.shape[1] == 0 or data.shape[1] == 1
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_meta_from_numpy(data, name, dtype, handle)
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|
|
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def _is_modin_series(data):
|
|
try:
|
|
import modin.pandas as pd
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, pd.Series)
|
|
|
|
|
|
def _from_pandas_series(
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data,
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|
missing: float,
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|
nthread: int,
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enable_categorical: bool,
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|
feature_names: Optional[List[str]],
|
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feature_types: Optional[List[str]],
|
|
):
|
|
from pandas.api.types import is_categorical_dtype
|
|
|
|
if (data.dtype.name not in _pandas_dtype_mapper) and not (
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is_categorical_dtype(data.dtype) and enable_categorical
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|
):
|
|
_invalid_dataframe_dtype(data)
|
|
if enable_categorical and is_categorical_dtype(data.dtype):
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data = data.cat.codes
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return _from_numpy_array(
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data.values.reshape(data.shape[0], 1).astype("float"),
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|
missing,
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nthread,
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feature_names,
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feature_types,
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)
|
|
|
|
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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(
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data,
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feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
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meta=None,
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meta_type=None,
|
|
):
|
|
"""Validate feature names and types if data table"""
|
|
if meta and data.shape[1] > 1:
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raise ValueError('DataTable for meta info cannot have multiple columns')
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|
if meta:
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|
meta_type = "float" if meta_type is None else meta_type
|
|
# 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
|
|
|
|
data_types_names = tuple(lt.name for lt in data.ltypes)
|
|
bad_fields = [data.names[i]
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|
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,
|
|
missing,
|
|
nthread,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
enable_categorical: bool,
|
|
) -> Tuple[ctypes.c_void_p, Optional[List[str]], Optional[List[str]]]:
|
|
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_cudf_df(data):
|
|
try:
|
|
import cudf
|
|
except ImportError:
|
|
return False
|
|
return hasattr(cudf, 'DataFrame') and isinstance(data, cudf.DataFrame)
|
|
|
|
|
|
def _cudf_array_interfaces(data, 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 = []
|
|
if _is_cudf_ser(data):
|
|
if is_categorical_dtype(data.dtype):
|
|
interface = cat_codes[0].__cuda_array_interface__
|
|
else:
|
|
interface = data.__cuda_array_interface__
|
|
if "mask" in interface:
|
|
interface["mask"] = interface["mask"].__cuda_array_interface__
|
|
interfaces.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__
|
|
if "mask" in interface:
|
|
interface["mask"] = interface["mask"].__cuda_array_interface__
|
|
interfaces.append(interface)
|
|
interfaces_str = bytes(json.dumps(interfaces, indent=2), "utf-8")
|
|
return interfaces_str
|
|
|
|
|
|
def _transform_cudf_df(
|
|
data,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
enable_categorical: bool,
|
|
):
|
|
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:
|
|
if is_categorical_dtype(data[col].dtype) and enable_categorical:
|
|
codes = data[col].cat.codes
|
|
cat_codes.append(codes)
|
|
|
|
return data, cat_codes, feature_names, feature_types
|
|
|
|
|
|
def _from_cudf_df(
|
|
data,
|
|
missing,
|
|
nthread,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
enable_categorical: bool,
|
|
) -> Tuple[ctypes.c_void_p, Any, Any]:
|
|
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):
|
|
try:
|
|
import cudf
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, cudf.Series)
|
|
|
|
|
|
def _is_cupy_array(data):
|
|
try:
|
|
import cupy
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, cupy.ndarray)
|
|
|
|
|
|
def _transform_cupy_array(data):
|
|
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,
|
|
missing,
|
|
nthread,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
):
|
|
"""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):
|
|
try:
|
|
import cupyx
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, cupyx.scipy.sparse.csr_matrix)
|
|
|
|
|
|
def _is_cupy_csc(data):
|
|
try:
|
|
import cupyx
|
|
except ImportError:
|
|
return False
|
|
return isinstance(data, cupyx.scipy.sparse.csc_matrix)
|
|
|
|
|
|
def _is_dlpack(data):
|
|
return 'PyCapsule' in str(type(data)) and "dltensor" in str(data)
|
|
|
|
|
|
def _transform_dlpack(data):
|
|
from cupy import fromDlpack # pylint: disable=E0401
|
|
assert 'used_dltensor' not in str(data)
|
|
data = fromDlpack(data)
|
|
return data
|
|
|
|
|
|
def _from_dlpack(
|
|
data,
|
|
missing,
|
|
nthread,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
):
|
|
data = _transform_dlpack(data)
|
|
return _from_cupy_array(data, missing, nthread, feature_names,
|
|
feature_types)
|
|
|
|
|
|
def _is_uri(data):
|
|
return isinstance(data, (str, os.PathLike))
|
|
|
|
|
|
def _from_uri(
|
|
data,
|
|
missing,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
):
|
|
_warn_unused_missing(data, missing)
|
|
handle = ctypes.c_void_p()
|
|
data = os.fspath(os.path.expanduser(data))
|
|
_check_call(_LIB.XGDMatrixCreateFromFile(c_str(data),
|
|
ctypes.c_int(1),
|
|
ctypes.byref(handle)))
|
|
return handle, feature_names, feature_types
|
|
|
|
|
|
def _is_list(data):
|
|
return isinstance(data, list)
|
|
|
|
|
|
def _from_list(
|
|
data,
|
|
missing,
|
|
n_threads,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
):
|
|
array = np.array(data)
|
|
_check_data_shape(data)
|
|
return _from_numpy_array(array, missing, n_threads, feature_names, feature_types)
|
|
|
|
|
|
def _is_tuple(data):
|
|
return isinstance(data, tuple)
|
|
|
|
|
|
def _from_tuple(
|
|
data,
|
|
missing,
|
|
n_threads,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
):
|
|
return _from_list(data, missing, n_threads, feature_names, feature_types)
|
|
|
|
|
|
def _is_iter(data):
|
|
return isinstance(data, DataIter)
|
|
|
|
|
|
def _has_array_protocol(data):
|
|
return hasattr(data, '__array__')
|
|
|
|
|
|
def _convert_unknown_data(data):
|
|
warnings.warn(
|
|
f'Unknown data type: {type(data)}, trying to convert it to csr_matrix',
|
|
UserWarning
|
|
)
|
|
try:
|
|
import scipy
|
|
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,
|
|
missing,
|
|
threads,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
enable_categorical: bool = False,
|
|
):
|
|
'''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, 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)
|
|
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_df(data):
|
|
return _from_pandas_df(data, enable_categorical, missing, threads,
|
|
feature_names, feature_types)
|
|
if _is_pandas_series(data):
|
|
return _from_pandas_series(
|
|
data, missing, threads, enable_categorical, 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 _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 _to_data_type(dtype: str, name: str):
|
|
dtype_map = {'float32': 1, 'float64': 2, 'uint32': 3, 'uint64': 4}
|
|
if dtype not in dtype_map:
|
|
raise TypeError(
|
|
f'Expecting float32, float64, uint32, uint64, got {dtype} ' +
|
|
f'for {name}.')
|
|
return dtype_map[dtype]
|
|
|
|
|
|
def _validate_meta_shape(data: Any, 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[Union[np.dtype, str]],
|
|
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, 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: 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, 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: str, dtype, handle: ctypes.c_void_p):
|
|
data, _, _ = _transform_dt_df(data, None, None, field, dtype)
|
|
_meta_from_numpy(data, field, dtype, handle)
|
|
|
|
|
|
def dispatch_meta_backend(
|
|
matrix: DMatrix, data, name: str, dtype: Optional[Union[str, np.dtype]] = 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):
|
|
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):
|
|
self.kwargs = kwargs
|
|
self.it = 0 # pylint: disable=invalid-name
|
|
super().__init__()
|
|
|
|
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,
|
|
feature_names: Optional[List[str]],
|
|
feature_types: Optional[List[str]],
|
|
enable_categorical: bool,
|
|
):
|
|
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_numpy_array(data):
|
|
return data, None, feature_names, feature_types
|
|
if _is_scipy_csr(data):
|
|
return data, None, feature_names, feature_types
|
|
if _is_pandas_df(data):
|
|
arr, feature_names, feature_types = _transform_pandas_df(
|
|
data, enable_categorical, feature_names, feature_types
|
|
)
|
|
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: Any,
|
|
cat_codes: Optional[list],
|
|
allow_host: bool,
|
|
) -> None:
|
|
"""Dispatch for DeviceQuantileDMatrix."""
|
|
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, cat_codes)
|
|
return
|
|
if _is_cudf_ser(data):
|
|
# pylint: disable=W0212
|
|
proxy._set_data_from_cuda_columnar(data, 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):
|
|
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
|