Add type hints to core.py (#7707)
Co-authored-by: Chengyang Gu <bridgream@gmail.com> Co-authored-by: jiamingy <jm.yuan@outlook.com>
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Chengyang
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python-package/xgboost/_typing.py
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60
python-package/xgboost/_typing.py
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@ -0,0 +1,60 @@
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"""Shared typing definition."""
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import ctypes
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import os
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from typing import Optional, List, Any, TypeVar, Union
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# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/dt.Frame/
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# cudf.DataFrame/cupy.array/dlpack
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DataType = Any
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# xgboost accepts some other possible types in practice due to historical reason, which is
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# lesser tested. For now we encourage users to pass a simple list of string.
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FeatureNames = Optional[List[str]]
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ArrayLike = Any
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PathLike = Union[str, os.PathLike]
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CupyT = ArrayLike # maybe need a stub for cupy arrays
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NumpyOrCupy = Any
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# ctypes
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# c_bst_ulong corresponds to bst_ulong defined in xgboost/c_api.h
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c_bst_ulong = ctypes.c_uint64 # pylint: disable=C0103
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CTypeT = Union[
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ctypes.c_void_p,
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ctypes.c_char_p,
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ctypes.c_int,
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ctypes.c_float,
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ctypes.c_uint,
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ctypes.c_size_t,
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]
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# supported numeric types
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CNumeric = Union[
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ctypes.c_float,
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ctypes.c_double,
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ctypes.c_uint,
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ctypes.c_uint64,
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ctypes.c_int32,
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ctypes.c_int64,
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]
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# c pointer types
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# real type should be, as defined in typeshed
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# but this has to be put in a .pyi file
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# c_str_ptr_t = ctypes.pointer[ctypes.c_char]
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CStrPtr = ctypes.pointer
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# c_str_pptr_t = ctypes.pointer[ctypes.c_char_p]
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CStrPptr = ctypes.pointer
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# c_float_ptr_t = ctypes.pointer[ctypes.c_float]
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CFloatPtr = ctypes.pointer
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# c_numeric_ptr_t = Union[
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# ctypes.pointer[ctypes.c_float], ctypes.pointer[ctypes.c_double],
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# ctypes.pointer[ctypes.c_uint], ctypes.pointer[ctypes.c_uint64],
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# ctypes.pointer[ctypes.c_int32], ctypes.pointer[ctypes.c_int64]
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# ]
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CNumericPtr = ctypes.pointer
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# template parameter
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_T = TypeVar("_T")
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@ -4,7 +4,7 @@
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from abc import ABC, abstractmethod
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from collections.abc import Mapping
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from typing import List, Optional, Any, Union, Dict, TypeVar
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from typing import Callable, Tuple, cast, Sequence
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from typing import Callable, Tuple, cast, Sequence, Type, Iterable
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import ctypes
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import os
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import re
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@ -17,22 +17,30 @@ from inspect import signature, Parameter
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import numpy as np
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import scipy.sparse
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from .compat import (STRING_TYPES, DataFrame, py_str, PANDAS_INSTALLED,
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lazy_isinstance)
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from .compat import STRING_TYPES, DataFrame, py_str, PANDAS_INSTALLED, lazy_isinstance
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from .libpath import find_lib_path
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# c_bst_ulong corresponds to bst_ulong defined in xgboost/c_api.h
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c_bst_ulong = ctypes.c_uint64
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# xgboost accepts some other possible types in practice due to historical reason, which is
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# lesser tested. For now we encourage users to pass a simple list of string.
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FeatNamesT = Optional[List[str]]
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from ._typing import (
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CStrPptr,
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c_bst_ulong,
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CNumeric,
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DataType,
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CNumericPtr,
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CStrPtr,
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CTypeT,
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ArrayLike,
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CFloatPtr,
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NumpyOrCupy,
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FeatureNames,
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_T,
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CupyT,
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)
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class XGBoostError(ValueError):
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"""Error thrown by xgboost trainer."""
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def from_pystr_to_cstr(data: Union[str, List[str]]):
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def from_pystr_to_cstr(data: Union[str, List[str]]) -> Union[bytes, CStrPptr]:
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"""Convert a Python str or list of Python str to C pointer
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Parameters
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@ -44,14 +52,14 @@ def from_pystr_to_cstr(data: Union[str, List[str]]):
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if isinstance(data, str):
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return bytes(data, "utf-8")
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if isinstance(data, list):
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pointers = (ctypes.c_char_p * len(data))()
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data = [bytes(d, 'utf-8') for d in data]
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pointers[:] = data
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pointers: ctypes.pointer = (ctypes.c_char_p * len(data))()
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data_as_bytes = [bytes(d, 'utf-8') for d in data]
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pointers[:] = data_as_bytes
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return pointers
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raise TypeError()
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def from_cstr_to_pystr(data, length) -> List[str]:
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def from_cstr_to_pystr(data: CStrPptr, length: c_bst_ulong) -> List[str]:
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"""Revert C pointer to Python str
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Parameters
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@ -64,9 +72,9 @@ def from_cstr_to_pystr(data, length) -> List[str]:
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res = []
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for i in range(length.value):
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try:
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res.append(str(data[i].decode('ascii')))
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res.append(str(data[i].decode('ascii'))) # type: ignore
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except UnicodeDecodeError:
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res.append(str(data[i].decode('utf-8')))
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res.append(str(data[i].decode('utf-8'))) # type: ignore
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return res
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@ -91,7 +99,7 @@ def _convert_ntree_limit(
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return iteration_range
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def _expect(expectations, got):
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def _expect(expectations: Sequence[Type], got: Type) -> str:
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"""Translate input error into string.
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Parameters
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@ -130,7 +138,7 @@ def _load_lib() -> ctypes.CDLL:
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lib_paths = find_lib_path()
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if not lib_paths:
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# This happens only when building document.
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return None # type: ignore
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return None # type: ignore
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try:
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pathBackup = os.environ['PATH'].split(os.pathsep)
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except KeyError:
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@ -167,7 +175,7 @@ Likely causes:
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Error message(s): {os_error_list}
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""")
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lib.XGBGetLastError.restype = ctypes.c_char_p
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lib.callback = _get_log_callback_func()
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lib.callback = _get_log_callback_func() # type: ignore
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if lib.XGBRegisterLogCallback(lib.callback) != 0:
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raise XGBoostError(lib.XGBGetLastError())
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return lib
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@ -192,7 +200,7 @@ def _check_call(ret: int) -> None:
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raise XGBoostError(py_str(_LIB.XGBGetLastError()))
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def _has_categorical(booster: "Booster", data: Any) -> bool:
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def _has_categorical(booster: "Booster", data: DataType) -> bool:
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"""Check whether the booster and input data for prediction contain categorical data.
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"""
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@ -224,8 +232,8 @@ def build_info() -> dict:
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return res
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def _numpy2ctypes_type(dtype):
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_NUMPY_TO_CTYPES_MAPPING = {
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def _numpy2ctypes_type(dtype: Type[np.number]) -> Type[CNumeric]:
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_NUMPY_TO_CTYPES_MAPPING: Dict[Type[np.number], Type[CNumeric]] = {
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np.float32: ctypes.c_float,
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np.float64: ctypes.c_double,
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np.uint32: ctypes.c_uint,
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@ -242,7 +250,7 @@ def _numpy2ctypes_type(dtype):
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return _NUMPY_TO_CTYPES_MAPPING[dtype]
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def _cuda_array_interface(data) -> bytes:
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def _cuda_array_interface(data: DataType) -> 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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@ -253,9 +261,9 @@ def _cuda_array_interface(data) -> bytes:
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return interface_str
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def ctypes2numpy(cptr, length, dtype) -> np.ndarray:
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def ctypes2numpy(cptr: CNumericPtr, length: int, dtype: Type[np.number]) -> np.ndarray:
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"""Convert a ctypes pointer array to a numpy array."""
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ctype = _numpy2ctypes_type(dtype)
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ctype: Type[CNumeric] = _numpy2ctypes_type(dtype)
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if not isinstance(cptr, ctypes.POINTER(ctype)):
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raise RuntimeError(f"expected {ctype} pointer")
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res = np.zeros(length, dtype=dtype)
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@ -264,7 +272,7 @@ def ctypes2numpy(cptr, length, dtype) -> np.ndarray:
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return res
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def ctypes2cupy(cptr, length, dtype):
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def ctypes2cupy(cptr: CNumericPtr, length: int, dtype: Type[np.number]) -> CupyT:
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"""Convert a ctypes pointer array to a cupy array."""
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# pylint: disable=import-error
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import cupy
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@ -290,7 +298,7 @@ def ctypes2cupy(cptr, length, dtype):
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return arr
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def ctypes2buffer(cptr, length) -> bytearray:
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def ctypes2buffer(cptr: CStrPtr, length: int) -> bytearray:
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"""Convert ctypes pointer to buffer type."""
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if not isinstance(cptr, ctypes.POINTER(ctypes.c_char)):
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raise RuntimeError('expected char pointer')
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@ -301,25 +309,30 @@ def ctypes2buffer(cptr, length) -> bytearray:
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return res
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def c_str(string):
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def c_str(string: str) -> ctypes.c_char_p:
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"""Convert a python string to cstring."""
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return ctypes.c_char_p(string.encode('utf-8'))
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def c_array(ctype, values):
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def c_array(ctype: Type[CTypeT], values: ArrayLike) -> ctypes.Array:
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"""Convert a python string to c array."""
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if isinstance(values, np.ndarray) and values.dtype.itemsize == ctypes.sizeof(ctype):
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return (ctype * len(values)).from_buffer_copy(values)
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return (ctype * len(values))(*values)
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def _prediction_output(shape, dims, predts, is_cuda):
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arr_shape: np.ndarray = ctypes2numpy(shape, dims.value, np.uint64)
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def _prediction_output(
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shape: CNumericPtr,
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dims: c_bst_ulong,
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predts: CFloatPtr,
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is_cuda: bool
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) -> NumpyOrCupy:
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arr_shape = ctypes2numpy(shape, dims.value, np.uint64)
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length = int(np.prod(arr_shape))
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if is_cuda:
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arr_predict = ctypes2cupy(predts, length, np.float32)
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else:
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arr_predict: np.ndarray = ctypes2numpy(predts, length, np.float32)
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arr_predict = ctypes2numpy(predts, length, np.float32)
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arr_predict = arr_predict.reshape(arr_shape)
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return arr_predict
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@ -415,7 +428,7 @@ class DataIter(ABC): # pylint: disable=too-many-instance-attributes
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def data_handle(
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data: Any,
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*,
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feature_names: FeatNamesT = None,
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feature_names: FeatureNames = None,
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feature_types: Optional[List[str]] = None,
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**kwargs: Any,
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) -> None:
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@ -472,7 +485,7 @@ class DataIter(ABC): # pylint: disable=too-many-instance-attributes
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# Nicolas Tresegnie
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# Sylvain Marie
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# License: BSD 3 clause
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def _deprecate_positional_args(f):
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def _deprecate_positional_args(f: Callable[..., _T]) -> Callable[..., _T]:
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"""Decorator for methods that issues warnings for positional arguments
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Using the keyword-only argument syntax in pep 3102, arguments after the
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@ -496,7 +509,7 @@ def _deprecate_positional_args(f):
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kwonly_args.append(name)
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@wraps(f)
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def inner_f(*args, **kwargs):
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def inner_f(*args: Any, **kwargs: Any) -> _T:
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extra_args = len(args) - len(all_args)
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if extra_args > 0:
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# ignore first 'self' argument for instance methods
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@ -529,21 +542,21 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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@_deprecate_positional_args
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def __init__(
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self,
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data,
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label=None,
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data: DataType,
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label: Optional[ArrayLike] = None,
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*,
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weight=None,
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base_margin=None,
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weight: Optional[ArrayLike] = None,
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base_margin: Optional[ArrayLike] = None,
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missing: Optional[float] = None,
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silent=False,
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feature_names: FeatNamesT = None,
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silent: bool = False,
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feature_names: FeatureNames = None,
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feature_types: Optional[List[str]] = None,
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nthread: Optional[int] = None,
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group=None,
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qid=None,
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label_lower_bound=None,
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label_upper_bound=None,
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feature_weights=None,
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group: Optional[ArrayLike] = None,
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qid: Optional[ArrayLike] = None,
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label_lower_bound: Optional[ArrayLike] = None,
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label_upper_bound: Optional[ArrayLike] = None,
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feature_weights: Optional[ArrayLike] = None,
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enable_categorical: bool = False,
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) -> None:
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"""Parameters
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@ -658,7 +671,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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"nthread": self.nthread,
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"cache_prefix": it.cache_prefix if it.cache_prefix else "",
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}
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args = from_pystr_to_cstr(json.dumps(args))
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args_cstr = from_pystr_to_cstr(json.dumps(args))
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handle = ctypes.c_void_p()
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reset_callback, next_callback = it.get_callbacks(
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True, enable_categorical
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@ -668,7 +681,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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it.proxy.handle,
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reset_callback,
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next_callback,
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args,
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args_cstr,
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ctypes.byref(handle),
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)
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it.reraise()
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@ -685,16 +698,16 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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def set_info(
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self,
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*,
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label=None,
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weight=None,
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base_margin=None,
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group=None,
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qid=None,
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label_lower_bound=None,
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label_upper_bound=None,
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feature_names: FeatNamesT = None,
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label: Optional[ArrayLike] = None,
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weight: Optional[ArrayLike] = None,
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base_margin: Optional[ArrayLike] = None,
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group: Optional[ArrayLike] = None,
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qid: Optional[ArrayLike] = None,
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label_lower_bound: Optional[ArrayLike] = None,
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label_upper_bound: Optional[ArrayLike] = None,
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feature_names: FeatureNames = None,
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feature_types: Optional[List[str]] = None,
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feature_weights=None
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feature_weights: Optional[ArrayLike] = None
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) -> None:
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"""Set meta info for DMatrix. See doc string for :py:obj:`xgboost.DMatrix`."""
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from .data import dispatch_meta_backend
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@ -763,7 +776,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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ctypes.byref(ret)))
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return ctypes2numpy(ret, length.value, np.uint32)
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def set_float_info(self, field: str, data) -> None:
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def set_float_info(self, field: str, data: ArrayLike) -> None:
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"""Set float type property into the DMatrix.
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Parameters
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@ -777,7 +790,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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from .data import dispatch_meta_backend
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dispatch_meta_backend(self, data, field, 'float')
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def set_float_info_npy2d(self, field: str, data) -> None:
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def set_float_info_npy2d(self, field: str, data: ArrayLike) -> None:
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"""Set float type property into the DMatrix
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for numpy 2d array input
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@ -792,7 +805,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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from .data import dispatch_meta_backend
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dispatch_meta_backend(self, data, field, 'float')
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def set_uint_info(self, field: str, data) -> None:
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def set_uint_info(self, field: str, data: ArrayLike) -> None:
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"""Set uint type property into the DMatrix.
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Parameters
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@ -806,7 +819,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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from .data import dispatch_meta_backend
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dispatch_meta_backend(self, data, field, 'uint32')
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def save_binary(self, fname, silent=True) -> None:
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def save_binary(self, fname: Union[str, os.PathLike], silent: bool = True) -> None:
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"""Save DMatrix to an XGBoost buffer. Saved binary can be later loaded
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by providing the path to :py:func:`xgboost.DMatrix` as input.
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@ -822,7 +835,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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c_str(fname),
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ctypes.c_int(silent)))
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def set_label(self, label) -> None:
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def set_label(self, label: ArrayLike) -> None:
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"""Set label of dmatrix
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Parameters
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@ -833,7 +846,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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from .data import dispatch_meta_backend
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dispatch_meta_backend(self, label, 'label', 'float')
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def set_weight(self, weight) -> None:
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def set_weight(self, weight: ArrayLike) -> None:
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"""Set weight of each instance.
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Parameters
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@ -852,7 +865,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
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from .data import dispatch_meta_backend
|
||||
dispatch_meta_backend(self, weight, 'weight', 'float')
|
||||
|
||||
def set_base_margin(self, margin) -> None:
|
||||
def set_base_margin(self, margin: ArrayLike) -> None:
|
||||
"""Set base margin of booster to start from.
|
||||
|
||||
This can be used to specify a prediction value of existing model to be
|
||||
@ -869,7 +882,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
|
||||
from .data import dispatch_meta_backend
|
||||
dispatch_meta_backend(self, margin, 'base_margin', 'float')
|
||||
|
||||
def set_group(self, group) -> None:
|
||||
def set_group(self, group: ArrayLike) -> None:
|
||||
"""Set group size of DMatrix (used for ranking).
|
||||
|
||||
Parameters
|
||||
@ -997,7 +1010,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
|
||||
return feature_names
|
||||
|
||||
@feature_names.setter
|
||||
def feature_names(self, feature_names: FeatNamesT) -> None:
|
||||
def feature_names(self, feature_names: FeatureNames) -> None:
|
||||
"""Set feature names (column labels).
|
||||
|
||||
Parameters
|
||||
@ -1026,9 +1039,9 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
|
||||
not any(x in f for x in set(('[', ']', '<')))
|
||||
for f in feature_names):
|
||||
raise ValueError('feature_names must be string, and may not contain [, ] or <')
|
||||
c_feature_names = [bytes(f, encoding='utf-8') for f in feature_names]
|
||||
feature_names_bytes = [bytes(f, encoding='utf-8') for f in feature_names]
|
||||
c_feature_names = (ctypes.c_char_p *
|
||||
len(c_feature_names))(*c_feature_names)
|
||||
len(feature_names_bytes))(*feature_names_bytes)
|
||||
_check_call(_LIB.XGDMatrixSetStrFeatureInfo(
|
||||
self.handle, c_str('feature_name'),
|
||||
c_feature_names,
|
||||
@ -1091,10 +1104,10 @@ class DMatrix: # pylint: disable=too-many-instance-attributes
|
||||
feature_types = [feature_types]
|
||||
except TypeError:
|
||||
feature_types = [feature_types]
|
||||
c_feature_types = [bytes(f, encoding='utf-8')
|
||||
feature_types_bytes = [bytes(f, encoding='utf-8')
|
||||
for f in feature_types]
|
||||
c_feature_types = (ctypes.c_char_p *
|
||||
len(c_feature_types))(*c_feature_types)
|
||||
len(feature_types_bytes))(*feature_types_bytes)
|
||||
_check_call(_LIB.XGDMatrixSetStrFeatureInfo(
|
||||
self.handle, c_str('feature_type'),
|
||||
c_feature_types,
|
||||
@ -1118,11 +1131,11 @@ class _ProxyDMatrix(DMatrix):
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self): # pylint: disable=super-init-not-called
|
||||
def __init__(self) -> None: # pylint: disable=super-init-not-called
|
||||
self.handle = ctypes.c_void_p()
|
||||
_check_call(_LIB.XGProxyDMatrixCreate(ctypes.byref(self.handle)))
|
||||
|
||||
def _set_data_from_cuda_interface(self, data) -> None:
|
||||
def _set_data_from_cuda_interface(self, data: DataType) -> None:
|
||||
"""Set data from CUDA array interface."""
|
||||
interface = data.__cuda_array_interface__
|
||||
interface_str = bytes(json.dumps(interface, indent=2), "utf-8")
|
||||
@ -1130,14 +1143,14 @@ class _ProxyDMatrix(DMatrix):
|
||||
_LIB.XGProxyDMatrixSetDataCudaArrayInterface(self.handle, interface_str)
|
||||
)
|
||||
|
||||
def _set_data_from_cuda_columnar(self, data, cat_codes: list) -> None:
|
||||
def _set_data_from_cuda_columnar(self, data: DataType, cat_codes: list) -> None:
|
||||
"""Set data from CUDA columnar format."""
|
||||
from .data import _cudf_array_interfaces
|
||||
|
||||
interfaces_str = _cudf_array_interfaces(data, cat_codes)
|
||||
_check_call(_LIB.XGProxyDMatrixSetDataCudaColumnar(self.handle, interfaces_str))
|
||||
|
||||
def _set_data_from_array(self, data: np.ndarray):
|
||||
def _set_data_from_array(self, data: np.ndarray) -> None:
|
||||
"""Set data from numpy array."""
|
||||
from .data import _array_interface
|
||||
|
||||
@ -1145,7 +1158,7 @@ class _ProxyDMatrix(DMatrix):
|
||||
_LIB.XGProxyDMatrixSetDataDense(self.handle, _array_interface(data))
|
||||
)
|
||||
|
||||
def _set_data_from_csr(self, csr):
|
||||
def _set_data_from_csr(self, csr: scipy.sparse.csr_matrix) -> None:
|
||||
"""Set data from scipy csr"""
|
||||
from .data import _array_interface
|
||||
|
||||
@ -1175,24 +1188,24 @@ class DeviceQuantileDMatrix(DMatrix):
|
||||
@_deprecate_positional_args
|
||||
def __init__( # pylint: disable=super-init-not-called
|
||||
self,
|
||||
data,
|
||||
label=None,
|
||||
data: DataType,
|
||||
label: Optional[ArrayLike] = None,
|
||||
*,
|
||||
weight=None,
|
||||
base_margin=None,
|
||||
missing=None,
|
||||
silent=False,
|
||||
feature_names: FeatNamesT = None,
|
||||
feature_types=None,
|
||||
weight: Optional[ArrayLike] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
missing: Optional[float] = None,
|
||||
silent: bool = False,
|
||||
feature_names: FeatureNames = None,
|
||||
feature_types: Optional[List[str]] = None,
|
||||
nthread: Optional[int] = None,
|
||||
max_bin: int = 256,
|
||||
group=None,
|
||||
qid=None,
|
||||
label_lower_bound=None,
|
||||
label_upper_bound=None,
|
||||
feature_weights=None,
|
||||
group: Optional[ArrayLike] = None,
|
||||
qid: Optional[ArrayLike] = None,
|
||||
label_lower_bound: Optional[ArrayLike] = None,
|
||||
label_upper_bound: Optional[ArrayLike] = None,
|
||||
feature_weights: Optional[ArrayLike] = None,
|
||||
enable_categorical: bool = False,
|
||||
):
|
||||
) -> None:
|
||||
self.max_bin = max_bin
|
||||
self.missing = missing if missing is not None else np.nan
|
||||
self.nthread = nthread if nthread is not None else 1
|
||||
@ -1223,7 +1236,7 @@ class DeviceQuantileDMatrix(DMatrix):
|
||||
enable_categorical=enable_categorical,
|
||||
)
|
||||
|
||||
def _init(self, data, enable_categorical: bool, **meta) -> None:
|
||||
def _init(self, data: DataType, enable_categorical: bool, **meta: Any) -> None:
|
||||
from .data import (
|
||||
_is_dlpack,
|
||||
_transform_dlpack,
|
||||
@ -1304,9 +1317,10 @@ def _configure_metrics(params: Union[Dict, List]) -> Union[Dict, List]:
|
||||
params = dict((k, v) for k, v in params.items())
|
||||
eval_metrics = params["eval_metric"]
|
||||
params.pop("eval_metric", None)
|
||||
params = list(params.items())
|
||||
params_list = list(params.items())
|
||||
for eval_metric in eval_metrics:
|
||||
params += [("eval_metric", eval_metric)]
|
||||
params_list += [("eval_metric", eval_metric)]
|
||||
return params_list
|
||||
return params
|
||||
|
||||
|
||||
@ -1417,7 +1431,7 @@ class Booster:
|
||||
"Constrained features are not a subset of training data feature names"
|
||||
) from e
|
||||
|
||||
def _configure_constraints(self, params: Union[Dict, List]) -> Union[Dict, List]:
|
||||
def _configure_constraints(self, params: Union[List, Dict]) -> Union[List, Dict]:
|
||||
if isinstance(params, dict):
|
||||
value = params.get("monotone_constraints")
|
||||
if value:
|
||||
@ -1546,7 +1560,7 @@ class Booster:
|
||||
def __copy__(self) -> "Booster":
|
||||
return self.__deepcopy__(None)
|
||||
|
||||
def __deepcopy__(self, _) -> "Booster":
|
||||
def __deepcopy__(self, _: Any) -> "Booster":
|
||||
'''Return a copy of booster.'''
|
||||
return Booster(model_file=self)
|
||||
|
||||
@ -1629,8 +1643,8 @@ class Booster:
|
||||
def _set_feature_info(self, features: Optional[List[str]], field: str) -> None:
|
||||
if features is not None:
|
||||
assert isinstance(features, list)
|
||||
c_feature_info = [bytes(f, encoding="utf-8") for f in features]
|
||||
c_feature_info = (ctypes.c_char_p * len(c_feature_info))(*c_feature_info)
|
||||
feature_info_bytes = [bytes(f, encoding="utf-8") for f in features]
|
||||
c_feature_info = (ctypes.c_char_p * len(feature_info_bytes))(*feature_info_bytes)
|
||||
_check_call(
|
||||
_LIB.XGBoosterSetStrFeatureInfo(
|
||||
self.handle, c_str(field), c_feature_info, c_bst_ulong(len(features))
|
||||
@ -1664,10 +1678,14 @@ class Booster:
|
||||
return self._get_feature_info("feature_name")
|
||||
|
||||
@feature_names.setter
|
||||
def feature_names(self, features: FeatNamesT) -> None:
|
||||
def feature_names(self, features: FeatureNames) -> None:
|
||||
self._set_feature_info(features, "feature_name")
|
||||
|
||||
def set_param(self, params, value=None):
|
||||
def set_param(
|
||||
self,
|
||||
params: Union[Dict, Iterable[Tuple[str, Any]], str],
|
||||
value: Optional[str] = None
|
||||
) -> None:
|
||||
"""Set parameters into the Booster.
|
||||
|
||||
Parameters
|
||||
@ -1966,14 +1984,14 @@ class Booster:
|
||||
|
||||
def inplace_predict(
|
||||
self,
|
||||
data: Any,
|
||||
data: DataType,
|
||||
iteration_range: Tuple[int, int] = (0, 0),
|
||||
predict_type: str = "value",
|
||||
missing: float = np.nan,
|
||||
validate_features: bool = True,
|
||||
base_margin: Any = None,
|
||||
strict_shape: bool = False
|
||||
):
|
||||
) -> NumpyOrCupy:
|
||||
"""Run prediction in-place, Unlike :py:meth:`predict` method, inplace prediction does not
|
||||
cache the prediction result.
|
||||
|
||||
@ -2232,11 +2250,11 @@ class Booster:
|
||||
raise TypeError('Unknown file type: ', fname)
|
||||
|
||||
if self.attr("best_iteration") is not None:
|
||||
self.best_iteration = int(self.attr("best_iteration"))
|
||||
self.best_iteration = int(self.attr("best_iteration")) # type: ignore
|
||||
if self.attr("best_score") is not None:
|
||||
self.best_score = float(self.attr("best_score"))
|
||||
self.best_score = float(self.attr("best_score")) # type: ignore
|
||||
if self.attr("best_ntree_limit") is not None:
|
||||
self.best_ntree_limit = int(self.attr("best_ntree_limit"))
|
||||
self.best_ntree_limit = int(self.attr("best_ntree_limit")) # type: ignore
|
||||
|
||||
def num_boosted_rounds(self) -> int:
|
||||
'''Get number of boosted rounds. For gblinear this is reset to 0 after
|
||||
@ -2255,7 +2273,8 @@ class Booster:
|
||||
_check_call(_LIB.XGBoosterGetNumFeature(self.handle, ctypes.byref(features)))
|
||||
return features.value
|
||||
|
||||
def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"):
|
||||
def dump_model(self, fout: Union[str, os.PathLike], fmap: Union[str, os.PathLike] = '',
|
||||
with_stats: bool = False, dump_format: str = "text") -> None:
|
||||
"""Dump model into a text or JSON file. Unlike :py:meth:`save_model`, the
|
||||
output format is primarily used for visualization or interpretation,
|
||||
hence it's more human readable but cannot be loaded back to XGBoost.
|
||||
@ -2274,24 +2293,25 @@ class Booster:
|
||||
if isinstance(fout, (STRING_TYPES, os.PathLike)):
|
||||
fout = os.fspath(os.path.expanduser(fout))
|
||||
# pylint: disable=consider-using-with
|
||||
fout = open(fout, 'w', encoding="utf-8")
|
||||
fout_obj = open(fout, 'w', encoding="utf-8")
|
||||
need_close = True
|
||||
else:
|
||||
fout_obj = fout
|
||||
need_close = False
|
||||
ret = self.get_dump(fmap, with_stats, dump_format)
|
||||
if dump_format == 'json':
|
||||
fout.write('[\n')
|
||||
fout_obj.write('[\n')
|
||||
for i, _ in enumerate(ret):
|
||||
fout.write(ret[i])
|
||||
fout_obj.write(ret[i])
|
||||
if i < len(ret) - 1:
|
||||
fout.write(",\n")
|
||||
fout.write('\n]')
|
||||
fout_obj.write(",\n")
|
||||
fout_obj.write('\n]')
|
||||
else:
|
||||
for i, _ in enumerate(ret):
|
||||
fout.write(f"booster[{i}]:\n")
|
||||
fout.write(ret[i])
|
||||
fout_obj.write(f"booster[{i}]:\n")
|
||||
fout_obj.write(ret[i])
|
||||
if need_close:
|
||||
fout.close()
|
||||
fout_obj.close()
|
||||
|
||||
def get_dump(
|
||||
self,
|
||||
@ -2438,11 +2458,11 @@ class Booster:
|
||||
tree_ids = []
|
||||
node_ids = []
|
||||
fids = []
|
||||
splits = []
|
||||
categories: List[Optional[float]] = []
|
||||
y_directs = []
|
||||
n_directs = []
|
||||
missings = []
|
||||
splits: List[Union[float, str]] = []
|
||||
categories: List[Union[Optional[float], List[str]]] = []
|
||||
y_directs: List[Union[float, str]] = []
|
||||
n_directs: List[Union[float, str]] = []
|
||||
missings: List[Union[float, str]] = []
|
||||
gains = []
|
||||
covers = []
|
||||
|
||||
@ -2483,9 +2503,9 @@ class Booster:
|
||||
# categorical
|
||||
parse = fid[0].split(":")
|
||||
cats = parse[1][1:-1] # strip the {}
|
||||
cats = cats.split(",")
|
||||
cats_split = cats.split(",")
|
||||
splits.append(float("NAN"))
|
||||
categories.append(cats if cats else None)
|
||||
categories.append(cats_split if cats_split else None)
|
||||
else:
|
||||
raise ValueError("Failed to parse model text dump.")
|
||||
stats = re.split('=|,', fid[1])
|
||||
|
||||
@ -57,7 +57,7 @@ from .compat import lazy_isinstance
|
||||
from .core import DMatrix, DeviceQuantileDMatrix, Booster, _expect, DataIter
|
||||
from .core import Objective, Metric
|
||||
from .core import _deprecate_positional_args, _has_categorical
|
||||
from .data import FeatNamesT
|
||||
from .data import FeatureNames
|
||||
from .training import train as worker_train
|
||||
from .tracker import RabitTracker, get_host_ip
|
||||
from .sklearn import XGBModel, XGBClassifier, XGBRegressorBase, XGBClassifierBase
|
||||
@ -326,7 +326,7 @@ class DaskDMatrix:
|
||||
base_margin: Optional[_DaskCollection] = None,
|
||||
missing: float = None,
|
||||
silent: bool = False, # pylint: disable=unused-argument
|
||||
feature_names: FeatNamesT = None,
|
||||
feature_names: FeatureNames = None,
|
||||
feature_types: Optional[List[str]] = None,
|
||||
group: Optional[_DaskCollection] = None,
|
||||
qid: Optional[_DaskCollection] = None,
|
||||
@ -602,7 +602,7 @@ class DaskPartitionIter(DataIter): # pylint: disable=R0902
|
||||
qid: Optional[List[Any]] = None,
|
||||
label_lower_bound: Optional[List[Any]] = None,
|
||||
label_upper_bound: Optional[List[Any]] = None,
|
||||
feature_names: FeatNamesT = None,
|
||||
feature_names: FeatureNames = None,
|
||||
feature_types: Optional[Union[Any, List[Any]]] = None,
|
||||
) -> None:
|
||||
self._data = data
|
||||
@ -645,7 +645,7 @@ class DaskPartitionIter(DataIter): # pylint: disable=R0902
|
||||
if self._iter == len(self._data):
|
||||
# Return 0 when there's no more batch.
|
||||
return 0
|
||||
feature_names: FeatNamesT = None
|
||||
feature_names: FeatureNames = None
|
||||
if self._feature_names:
|
||||
feature_names = self._feature_names
|
||||
else:
|
||||
@ -696,7 +696,7 @@ class DaskDeviceQuantileDMatrix(DaskDMatrix):
|
||||
base_margin: Optional[_DaskCollection] = None,
|
||||
missing: float = None,
|
||||
silent: bool = False, # disable=unused-argument
|
||||
feature_names: FeatNamesT = None,
|
||||
feature_names: FeatureNames = None,
|
||||
feature_types: Optional[Union[Any, List[Any]]] = None,
|
||||
max_bin: int = 256,
|
||||
group: Optional[_DaskCollection] = None,
|
||||
@ -733,7 +733,7 @@ class DaskDeviceQuantileDMatrix(DaskDMatrix):
|
||||
|
||||
|
||||
def _create_device_quantile_dmatrix(
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[Union[Any, List[Any]]],
|
||||
feature_weights: Optional[Any],
|
||||
missing: float,
|
||||
@ -774,7 +774,7 @@ def _create_device_quantile_dmatrix(
|
||||
|
||||
|
||||
def _create_dmatrix(
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[Union[Any, List[Any]]],
|
||||
feature_weights: Optional[Any],
|
||||
missing: float,
|
||||
|
||||
@ -12,7 +12,7 @@ import numpy as np
|
||||
|
||||
from .core import c_array, _LIB, _check_call, c_str
|
||||
from .core import _cuda_array_interface
|
||||
from .core import DataIter, _ProxyDMatrix, DMatrix, FeatNamesT
|
||||
from .core import DataIter, _ProxyDMatrix, DMatrix, FeatureNames
|
||||
from .compat import lazy_isinstance, DataFrame
|
||||
|
||||
c_bst_ulong = ctypes.c_uint64 # pylint: disable=invalid-name
|
||||
@ -69,7 +69,7 @@ def _from_scipy_csr(
|
||||
data,
|
||||
missing,
|
||||
nthread,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
"""Initialize data from a CSR matrix."""
|
||||
@ -108,7 +108,7 @@ def _is_scipy_csc(data):
|
||||
def _from_scipy_csc(
|
||||
data,
|
||||
missing,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
if len(data.indices) != len(data.data):
|
||||
@ -164,7 +164,7 @@ def _from_numpy_array(
|
||||
data,
|
||||
missing,
|
||||
nthread,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
"""Initialize data from a 2-D numpy matrix.
|
||||
@ -245,11 +245,11 @@ be set to `True`.""" + err
|
||||
def _transform_pandas_df(
|
||||
data: DataFrame,
|
||||
enable_categorical: bool,
|
||||
feature_names: FeatNamesT = None,
|
||||
feature_names: FeatureNames = None,
|
||||
feature_types: Optional[List[str]] = None,
|
||||
meta: Optional[str] = None,
|
||||
meta_type: Optional[str] = None,
|
||||
) -> Tuple[np.ndarray, FeatNamesT, Optional[List[str]]]:
|
||||
) -> Tuple[np.ndarray, FeatureNames, Optional[List[str]]]:
|
||||
import pandas as pd
|
||||
from pandas.api.types import is_sparse, is_categorical_dtype
|
||||
|
||||
@ -313,9 +313,9 @@ def _from_pandas_df(
|
||||
enable_categorical: bool,
|
||||
missing: float,
|
||||
nthread: int,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
) -> Tuple[ctypes.c_void_p, FeatNamesT, Optional[List[str]]]:
|
||||
) -> Tuple[ctypes.c_void_p, FeatureNames, Optional[List[str]]]:
|
||||
data, feature_names, feature_types = _transform_pandas_df(
|
||||
data, enable_categorical, feature_names, feature_types
|
||||
)
|
||||
@ -355,7 +355,7 @@ def _from_pandas_series(
|
||||
missing: float,
|
||||
nthread: int,
|
||||
enable_categorical: bool,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
from pandas.api.types import is_categorical_dtype
|
||||
@ -386,7 +386,7 @@ _dt_type_mapper2 = {'bool': 'i', 'int': 'int', 'real': 'float'}
|
||||
|
||||
def _transform_dt_df(
|
||||
data,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
meta=None,
|
||||
meta_type=None,
|
||||
@ -427,10 +427,10 @@ def _from_dt_df(
|
||||
data,
|
||||
missing,
|
||||
nthread,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
enable_categorical: bool,
|
||||
) -> Tuple[ctypes.c_void_p, FeatNamesT, Optional[List[str]]]:
|
||||
) -> Tuple[ctypes.c_void_p, FeatureNames, Optional[List[str]]]:
|
||||
if enable_categorical:
|
||||
raise ValueError("categorical data in datatable is not supported yet.")
|
||||
data, feature_names, feature_types = _transform_dt_df(
|
||||
@ -594,7 +594,7 @@ def _cudf_array_interfaces(data, cat_codes: list) -> bytes:
|
||||
|
||||
def _transform_cudf_df(
|
||||
data,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
enable_categorical: bool,
|
||||
):
|
||||
@ -660,7 +660,7 @@ def _from_cudf_df(
|
||||
data,
|
||||
missing,
|
||||
nthread,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
enable_categorical: bool,
|
||||
) -> Tuple[ctypes.c_void_p, Any, Any]:
|
||||
@ -710,7 +710,7 @@ def _from_cupy_array(
|
||||
data,
|
||||
missing,
|
||||
nthread,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
"""Initialize DMatrix from cupy ndarray."""
|
||||
@ -757,7 +757,7 @@ def _from_dlpack(
|
||||
data,
|
||||
missing,
|
||||
nthread,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
data = _transform_dlpack(data)
|
||||
@ -772,7 +772,7 @@ def _is_uri(data):
|
||||
def _from_uri(
|
||||
data,
|
||||
missing,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
_warn_unused_missing(data, missing)
|
||||
@ -792,7 +792,7 @@ def _from_list(
|
||||
data,
|
||||
missing,
|
||||
n_threads,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
array = np.array(data)
|
||||
@ -808,7 +808,7 @@ def _from_tuple(
|
||||
data,
|
||||
missing,
|
||||
n_threads,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
):
|
||||
return _from_list(data, missing, n_threads, feature_names, feature_types)
|
||||
@ -844,7 +844,7 @@ def dispatch_data_backend(
|
||||
data,
|
||||
missing,
|
||||
threads,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
enable_categorical: bool = False,
|
||||
):
|
||||
@ -1076,7 +1076,7 @@ class SingleBatchInternalIter(DataIter): # pylint: disable=R0902
|
||||
|
||||
def _proxy_transform(
|
||||
data,
|
||||
feature_names: FeatNamesT,
|
||||
feature_names: FeatureNames,
|
||||
feature_types: Optional[List[str]],
|
||||
enable_categorical: bool,
|
||||
):
|
||||
|
||||
@ -14,6 +14,7 @@ from .core import Metric
|
||||
from .training import train
|
||||
from .callback import TrainingCallback
|
||||
from .data import _is_cudf_df, _is_cudf_ser, _is_cupy_array
|
||||
from ._typing import ArrayLike
|
||||
|
||||
# Do not use class names on scikit-learn directly. Re-define the classes on
|
||||
# .compat to guarantee the behavior without scikit-learn
|
||||
@ -25,8 +26,6 @@ from .compat import (
|
||||
XGBoostLabelEncoder,
|
||||
)
|
||||
|
||||
array_like = Any
|
||||
|
||||
|
||||
class XGBRankerMixIn: # pylint: disable=too-few-public-methods
|
||||
"""MixIn for ranking, defines the _estimator_type usually defined in scikit-learn base
|
||||
@ -862,19 +861,19 @@ class XGBModel(XGBModelBase):
|
||||
@_deprecate_positional_args
|
||||
def fit(
|
||||
self,
|
||||
X: array_like,
|
||||
y: array_like,
|
||||
X: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*,
|
||||
sample_weight: Optional[array_like] = None,
|
||||
base_margin: Optional[array_like] = None,
|
||||
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
||||
sample_weight: Optional[ArrayLike] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
|
||||
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
||||
early_stopping_rounds: Optional[int] = None,
|
||||
verbose: Optional[bool] = True,
|
||||
xgb_model: Optional[Union[Booster, str, "XGBModel"]] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
||||
feature_weights: Optional[array_like] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
feature_weights: Optional[ArrayLike] = None,
|
||||
callbacks: Optional[Sequence[TrainingCallback]] = None
|
||||
) -> "XGBModel":
|
||||
# pylint: disable=invalid-name,attribute-defined-outside-init
|
||||
@ -1001,11 +1000,11 @@ class XGBModel(XGBModelBase):
|
||||
|
||||
def predict(
|
||||
self,
|
||||
X: array_like,
|
||||
X: ArrayLike,
|
||||
output_margin: bool = False,
|
||||
ntree_limit: Optional[int] = None,
|
||||
validate_features: bool = True,
|
||||
base_margin: Optional[array_like] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
iteration_range: Optional[Tuple[int, int]] = None,
|
||||
) -> np.ndarray:
|
||||
"""Predict with `X`. If the model is trained with early stopping, then `best_iteration`
|
||||
@ -1077,7 +1076,7 @@ class XGBModel(XGBModelBase):
|
||||
)
|
||||
|
||||
def apply(
|
||||
self, X: array_like,
|
||||
self, X: ArrayLike,
|
||||
ntree_limit: int = 0,
|
||||
iteration_range: Optional[Tuple[int, int]] = None
|
||||
) -> np.ndarray:
|
||||
@ -1317,19 +1316,19 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
|
||||
@_deprecate_positional_args
|
||||
def fit(
|
||||
self,
|
||||
X: array_like,
|
||||
y: array_like,
|
||||
X: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*,
|
||||
sample_weight: Optional[array_like] = None,
|
||||
base_margin: Optional[array_like] = None,
|
||||
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
||||
sample_weight: Optional[ArrayLike] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
|
||||
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
||||
early_stopping_rounds: Optional[int] = None,
|
||||
verbose: Optional[bool] = True,
|
||||
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
||||
feature_weights: Optional[array_like] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
feature_weights: Optional[ArrayLike] = None,
|
||||
callbacks: Optional[Sequence[TrainingCallback]] = None
|
||||
) -> "XGBClassifier":
|
||||
# pylint: disable = attribute-defined-outside-init,too-many-statements
|
||||
@ -1425,11 +1424,11 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
|
||||
|
||||
def predict(
|
||||
self,
|
||||
X: array_like,
|
||||
X: ArrayLike,
|
||||
output_margin: bool = False,
|
||||
ntree_limit: Optional[int] = None,
|
||||
validate_features: bool = True,
|
||||
base_margin: Optional[array_like] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
iteration_range: Optional[Tuple[int, int]] = None,
|
||||
) -> np.ndarray:
|
||||
class_probs = super().predict(
|
||||
@ -1464,10 +1463,10 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
|
||||
|
||||
def predict_proba(
|
||||
self,
|
||||
X: array_like,
|
||||
X: ArrayLike,
|
||||
ntree_limit: Optional[int] = None,
|
||||
validate_features: bool = True,
|
||||
base_margin: Optional[array_like] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
iteration_range: Optional[Tuple[int, int]] = None,
|
||||
) -> np.ndarray:
|
||||
""" Predict the probability of each `X` example being of a given class.
|
||||
@ -1558,19 +1557,19 @@ class XGBRFClassifier(XGBClassifier):
|
||||
@_deprecate_positional_args
|
||||
def fit(
|
||||
self,
|
||||
X: array_like,
|
||||
y: array_like,
|
||||
X: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*,
|
||||
sample_weight: Optional[array_like] = None,
|
||||
base_margin: Optional[array_like] = None,
|
||||
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
||||
sample_weight: Optional[ArrayLike] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
|
||||
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
||||
early_stopping_rounds: Optional[int] = None,
|
||||
verbose: Optional[bool] = True,
|
||||
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
||||
feature_weights: Optional[array_like] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
feature_weights: Optional[ArrayLike] = None,
|
||||
callbacks: Optional[Sequence[TrainingCallback]] = None
|
||||
) -> "XGBRFClassifier":
|
||||
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
||||
@ -1630,19 +1629,19 @@ class XGBRFRegressor(XGBRegressor):
|
||||
@_deprecate_positional_args
|
||||
def fit(
|
||||
self,
|
||||
X: array_like,
|
||||
y: array_like,
|
||||
X: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*,
|
||||
sample_weight: Optional[array_like] = None,
|
||||
base_margin: Optional[array_like] = None,
|
||||
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
||||
sample_weight: Optional[ArrayLike] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
|
||||
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
||||
early_stopping_rounds: Optional[int] = None,
|
||||
verbose: Optional[bool] = True,
|
||||
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
||||
feature_weights: Optional[array_like] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
feature_weights: Optional[ArrayLike] = None,
|
||||
callbacks: Optional[Sequence[TrainingCallback]] = None
|
||||
) -> "XGBRFRegressor":
|
||||
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
||||
@ -1705,23 +1704,23 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
|
||||
@_deprecate_positional_args
|
||||
def fit(
|
||||
self,
|
||||
X: array_like,
|
||||
y: array_like,
|
||||
X: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*,
|
||||
group: Optional[array_like] = None,
|
||||
qid: Optional[array_like] = None,
|
||||
sample_weight: Optional[array_like] = None,
|
||||
base_margin: Optional[array_like] = None,
|
||||
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
||||
eval_group: Optional[Sequence[array_like]] = None,
|
||||
eval_qid: Optional[Sequence[array_like]] = None,
|
||||
group: Optional[ArrayLike] = None,
|
||||
qid: Optional[ArrayLike] = None,
|
||||
sample_weight: Optional[ArrayLike] = None,
|
||||
base_margin: Optional[ArrayLike] = None,
|
||||
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
|
||||
eval_group: Optional[Sequence[ArrayLike]] = None,
|
||||
eval_qid: Optional[Sequence[ArrayLike]] = None,
|
||||
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
||||
early_stopping_rounds: Optional[int] = None,
|
||||
verbose: Optional[bool] = False,
|
||||
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
||||
feature_weights: Optional[array_like] = None,
|
||||
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
|
||||
feature_weights: Optional[ArrayLike] = None,
|
||||
callbacks: Optional[Sequence[TrainingCallback]] = None
|
||||
) -> "XGBRanker":
|
||||
# pylint: disable = attribute-defined-outside-init,arguments-differ
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user