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data.py
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data.py
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# pylint: disable=too-many-arguments, too-many-branches, too-many-lines
# pylint: disable=too-many-return-statements, import-error
'''Data dispatching for DMatrix.'''
import ctypes
import json
import os
import warnings
from typing import Any, Callable, Iterator, List, Optional, Sequence, Tuple, Union, cast
import numpy as np
from ._typing import (
CupyT,
DataType,
FeatureNames,
FeatureTypes,
FloatCompatible,
NumpyDType,
PandasDType,
c_bst_ulong,
)
from .compat import DataFrame, lazy_isinstance
from .core import (
_LIB,
DataIter,
DMatrix,
_check_call,
_cuda_array_interface,
_ProxyDMatrix,
c_array,
c_str,
from_pystr_to_cstr,
)
DispatchedDataBackendReturnType = Tuple[
ctypes.c_void_p, Optional[FeatureNames], Optional[FeatureTypes]]
CAT_T = "c"
# meta info that can be a matrix instead of vector.
_matrix_meta = {"base_margin", "label"}
def _warn_unused_missing(data: DataType, missing: Optional[FloatCompatible]) -> None:
if (missing is not None) and (not np.isnan(missing)):
warnings.warn(
'`missing` is not used for current input data type:' +
str(type(data)), UserWarning)
def _check_complex(data: DataType) -> None:
'''Test whether data is complex using `dtype` attribute.'''
complex_dtypes = (np.complex128, np.complex64,
np.cfloat, np.cdouble, np.clongdouble)
if hasattr(data, 'dtype') and data.dtype in complex_dtypes:
raise ValueError('Complex data not supported')
def _check_data_shape(data: DataType) -> None:
if hasattr(data, "shape") and len(data.shape) != 2:
raise ValueError("Please reshape the input data into 2-dimensional matrix.")
def _is_scipy_csr(data: DataType) -> bool:
try:
import scipy.sparse
except ImportError:
return False
return isinstance(data, scipy.sparse.csr_matrix)
def _array_interface(data: np.ndarray) -> bytes:
assert (
data.dtype.hasobject is False
), "Input data contains `object` dtype. Expecting numeric data."
interface = data.__array_interface__
if "mask" in interface:
interface["mask"] = interface["mask"].__array_interface__
interface_str = bytes(json.dumps(interface), "utf-8")
return interface_str
def _from_scipy_csr(
data: DataType,
missing: FloatCompatible,
nthread: int,
feature_names: Optional[FeatureNames],
feature_types: Optional[FeatureTypes],
) -> DispatchedDataBackendReturnType:
"""Initialize data from a CSR matrix."""
if len(data.indices) != len(data.data):
raise ValueError(
f"length mismatch: {len(data.indices)} vs {len(data.data)}"
)
handle = ctypes.c_void_p()
args = {
"missing": float(missing),
"nthread": int(nthread),
}
config = bytes(json.dumps(args), "utf-8")
_check_call(
_LIB.XGDMatrixCreateFromCSR(
_array_interface(data.indptr),
_array_interface(data.indices),
_array_interface(data.data),
c_bst_ulong(data.shape[1]),
config,
ctypes.byref(handle),
)
)
return handle, feature_names, feature_types
def _is_scipy_csc(data: DataType) -> bool:
try:
import scipy.sparse
except ImportError:
return False
return isinstance(data, scipy.sparse.csc_matrix)
def _from_scipy_csc(
data: DataType,
missing: Optional[FloatCompatible],
feature_names: Optional[FeatureNames],
feature_types: Optional[FeatureTypes],
) -> DispatchedDataBackendReturnType:
if len(data.indices) != len(data.data):
raise ValueError(f"length mismatch: {len(data.indices)} vs {len(data.data)}")
_warn_unused_missing(data, missing)
handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromCSCEx(
c_array(ctypes.c_size_t, data.indptr),
c_array(ctypes.c_uint, data.indices),
c_array(ctypes.c_float, data.data),
ctypes.c_size_t(len(data.indptr)),
ctypes.c_size_t(len(data.data)),
ctypes.c_size_t(data.shape[0]),
ctypes.byref(handle)))
return handle, feature_names, feature_types
def _is_scipy_coo(data: DataType) -> bool:
try:
import scipy.sparse
except ImportError:
return False
return isinstance(data, scipy.sparse.coo_matrix)
def _is_numpy_array(data: DataType) -> bool:
return isinstance(data, (np.ndarray, np.matrix))
def _ensure_np_dtype(
data: DataType,
dtype: Optional[NumpyDType]
) -> Tuple[np.ndarray, Optional[NumpyDType]]:
if data.dtype.hasobject or data.dtype in [np.float16, np.bool_]:
data = data.astype(np.float32, copy=False)
dtype = np.float32
return data, dtype
def _maybe_np_slice(data: DataType, dtype: Optional[NumpyDType]) -> np.ndarray:
'''Handle numpy slice. This can be removed if we use __array_interface__.
'''
try:
if not data.flags.c_contiguous:
data = np.array(data, copy=True, dtype=dtype)
else:
data = np.array(data, copy=False, dtype=dtype)
except AttributeError:
data = np.array(data, copy=False, dtype=dtype)
data, dtype = _ensure_np_dtype(data, dtype)
return data
def _from_numpy_array(
data: DataType,
missing: FloatCompatible,
nthread: int,
feature_names: Optional[FeatureNames],
feature_types: Optional[FeatureTypes],
) -> DispatchedDataBackendReturnType:
"""Initialize data from a 2-D numpy matrix.
"""
if len(data.shape) != 2:
raise ValueError(
"Expecting 2 dimensional numpy.ndarray, got: ", data.shape
)
data, _ = _ensure_np_dtype(data, data.dtype)
handle = ctypes.c_void_p()
args = {
"missing": float(missing),
"nthread": int(nthread),
}
config = bytes(json.dumps(args), "utf-8")
_check_call(
_LIB.XGDMatrixCreateFromDense(
_array_interface(data),
config,
ctypes.byref(handle),
)
)
return handle, feature_names, feature_types
def _is_pandas_df(data: DataType) -> bool:
try:
import pandas as pd
except ImportError:
return False
return isinstance(data, pd.DataFrame)
def _is_modin_df(data: DataType) -> bool:
try:
import modin.pandas as pd
except ImportError:
return False
return isinstance(data, pd.DataFrame)
_pandas_dtype_mapper = {
'int8': 'int',
'int16': 'int',
'int32': 'int',
'int64': 'int',
'uint8': 'int',
'uint16': 'int',
'uint32': 'int',
'uint64': 'int',
'float16': 'float',
'float32': 'float',
'float64': 'float',
'bool': 'i',
# nullable types
"Int16": "int",
"Int32": "int",
"Int64": "int",
"Float32": "float",
"Float64": "float",
"boolean": "i",
}
_ENABLE_CAT_ERR = (
"When categorical type is supplied, The experimental DMatrix parameter"
"`enable_categorical` must be set to `True`."
)
def _invalid_dataframe_dtype(data: DataType) -> None:
# pandas series has `dtypes` but it's just a single object
# cudf series doesn't have `dtypes`.
if hasattr(data, "dtypes") and hasattr(data.dtypes, "__iter__"):
bad_fields = [
f"{data.columns[i]}: {dtype}"
for i, dtype in enumerate(data.dtypes)
if dtype.name not in _pandas_dtype_mapper
]
err = " Invalid columns:" + ", ".join(bad_fields)
else:
err = ""
type_err = "DataFrame.dtypes for data must be int, float, bool or category."
msg = f"""{type_err} {_ENABLE_CAT_ERR} {err}"""
raise ValueError(msg)
def _pandas_feature_info(
data: DataFrame,
meta: Optional[str],
feature_names: Optional[FeatureNames],
feature_types: Optional[FeatureTypes],
enable_categorical: bool,
) -> Tuple[Optional[FeatureNames], Optional[FeatureTypes]]:
import pandas as pd
from pandas.api.types import (
is_sparse,
is_categorical_dtype,
)
# handle feature names
if feature_names is None and meta is None:
if isinstance(data.columns, pd.MultiIndex):
feature_names = [" ".join([str(x) for x in i]) for i in data.columns]
elif isinstance(data.columns, (pd.Index, pd.RangeIndex)):
feature_names = list(map(str, data.columns))
else:
feature_names = data.columns.format()
# handle feature types
if feature_types is None and meta is None:
feature_types = []
for dtype in data.dtypes:
if is_sparse(dtype):
feature_types.append(_pandas_dtype_mapper[dtype.subtype.name])
elif is_categorical_dtype(dtype) and enable_categorical:
feature_types.append(CAT_T)
else:
feature_types.append(_pandas_dtype_mapper[dtype.name])
return feature_names, feature_types
def is_nullable_dtype(dtype: PandasDType) -> bool:
"""Wether dtype is a pandas nullable type."""
from pandas.api.types import (
is_integer_dtype,
is_bool_dtype,
is_float_dtype,
is_categorical_dtype,
)
# dtype: pd.core.arrays.numeric.NumericDtype
nullable_alias = {"Int16", "Int32", "Int64", "Float32", "Float64", "category"}
is_int = is_integer_dtype(dtype) and dtype.name in nullable_alias
# np.bool has alias `bool`, while pd.BooleanDtype has `bzoolean`.
is_bool = is_bool_dtype(dtype) and dtype.name == "boolean"
is_float = is_float_dtype(dtype) and dtype.name in nullable_alias
return is_int or is_bool or is_float or is_categorical_dtype(dtype)
def _pandas_cat_null(data: DataFrame) -> DataFrame:
from pandas.api.types import is_categorical_dtype
# handle category codes and nullable.
cat_columns = [
col
for col, dtype in zip(data.columns, data.dtypes)
if is_categorical_dtype(dtype)
]
nul_columns = [
col for col, dtype in zip(data.columns, data.dtypes) if is_nullable_dtype(dtype)
]
if cat_columns or nul_columns:
# Avoid transformation due to: PerformanceWarning: DataFrame is highly
# fragmented
transformed = data.copy()
else:
transformed = data
if cat_columns:
# DF doesn't have the cat attribute, so we use apply here
transformed[cat_columns] = (
transformed[cat_columns]
.apply(lambda x: x.cat.codes)
.astype(np.float32)
.replace(-1.0, np.NaN)
)
if nul_columns:
transformed[nul_columns] = transformed[nul_columns].astype(np.float32)
return transformed
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_sparse,
is_categorical_dtype,
)
if not all(
dtype.name in _pandas_dtype_mapper
or is_sparse(dtype)
or (is_nullable_dtype(dtype) and not is_categorical_dtype(dtype))
or (is_categorical_dtype(dtype) and enable_categorical)
for dtype in data.dtypes
):
_invalid_dataframe_dtype(data)
feature_names, feature_types = _pandas_feature_info(
data, meta, feature_names, feature_types, enable_categorical
)
transformed = _pandas_cat_null(data)
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')
_dt_type_mapper = {'bool': 'bool', 'int': 'int', 'real': 'float'}
_dt_type_mapper2 = {'bool': 'i', 'int': 'int', 'real': 'float'}
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"""
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],
) -> DispatchedDataBackendReturnType:
_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: 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,
) -> 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, 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 _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)))