id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
322,600 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesAll | class DTypesAll(DTypesBool, DTypesNumeric):
pass | class DTypesAll(DTypesBool, DTypesNumeric):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
322,601 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesBool | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class DTypesBool(TypedDict):
bool: DType |
class DTypesBool(TypedDict):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 1 | 0 | 0 |
322,602 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesComplex | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class DTypesComplex(TypedDict):
complex64: DType
complex128: DType |
class DTypesComplex(TypedDict):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 1 | 2 | 0 | 3 | 1 | 2 | 0 | 1 | 0 | 0 |
322,603 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesIntegral | class DTypesIntegral(DTypesSigned, DTypesUnsigned):
pass | class DTypesIntegral(DTypesSigned, DTypesUnsigned):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
322,604 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesNumeric | class DTypesNumeric(DTypesIntegral, DTypesReal, DTypesComplex):
pass | class DTypesNumeric(DTypesIntegral, DTypesReal, DTypesComplex):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 3 | 0 | 0 |
322,605 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesReal | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class DTypesReal(TypedDict):
float32: DType
float64: DType |
class DTypesReal(TypedDict):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 1 | 2 | 0 | 3 | 1 | 2 | 0 | 1 | 0 | 0 |
322,606 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesSigned | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class DTypesSigned(TypedDict):
int8: DType
int16: DType
int32: DType
int64: DType |
class DTypesSigned(TypedDict):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 1 | 4 | 0 | 5 | 1 | 4 | 0 | 1 | 0 | 0 |
322,607 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.DTypesUnsigned | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class DTypesUnsigned(TypedDict):
uint8: DType
uint16: DType
uint32: DType
uint64: DType |
class DTypesUnsigned(TypedDict):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 1 | 4 | 0 | 5 | 1 | 4 | 0 | 1 | 0 | 0 |
322,608 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.HasShape | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class HasShape(Protocol[_T_co]):
@property
def shape(self, /) -> _T_co:
... |
class HasShape(Protocol[_T_co]):
@property
def shape(self, /) -> _T_co:
pass | 3 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 25 | 3 | 0 | 3 | 3 | 1 | 0 | 3 | 2 | 1 | 1 | 5 | 0 | 1 |
322,609 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.JustComplex | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
@final
class JustComplex(Protocol):
@property
def __class__(self, /) -> type[complex]:
...
@__class__.setter
def __class__(self, value: type[complex], /) -> None:
... | @final
class JustComplex(Protocol):
@property
def __class__(self, /) -> type[complex]:
pass
@__class__.setter
def __class__(self, /) -> type[complex]:
pass | 6 | 0 | 1 | 0 | 1 | 1 | 1 | 0.2 | 1 | 1 | 0 | 0 | 2 | 0 | 2 | 26 | 5 | 0 | 5 | 5 | 2 | 1 | 5 | 3 | 2 | 1 | 5 | 0 | 2 |
322,610 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.JustFloat | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
@final
class JustFloat(Protocol):
@property
def __class__(self, /) -> type[float]:
...
@__class__.setter
def __class__(self, value: type[float], /) -> None:
... | @final
class JustFloat(Protocol):
@property
def __class__(self, /) -> type[float]:
pass
@__class__.setter
def __class__(self, /) -> type[float]:
pass | 6 | 0 | 1 | 0 | 1 | 1 | 1 | 0.2 | 1 | 1 | 0 | 0 | 2 | 0 | 2 | 26 | 5 | 0 | 5 | 5 | 2 | 1 | 5 | 3 | 2 | 1 | 5 | 0 | 2 |
322,611 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.JustInt | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
@final
class JustInt(Protocol):
@property
def __class__(self, /) -> type[int]:
...
@__class__.setter
def __class__(self, value: type[int], /) -> None:
... | @final
class JustInt(Protocol):
@property
def __class__(self, /) -> type[int]:
pass
@__class__.setter
def __class__(self, /) -> type[int]:
pass | 6 | 0 | 1 | 0 | 1 | 1 | 1 | 0.2 | 1 | 1 | 0 | 0 | 2 | 0 | 2 | 26 | 5 | 0 | 5 | 5 | 2 | 1 | 5 | 3 | 2 | 1 | 5 | 0 | 2 |
322,612 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.NestedSequence | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class NestedSequence(Protocol[_T_co]):
def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]:
...
def __len__(self, /) -> int:
... |
class NestedSequence(Protocol[_T_co]):
def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]:
pass
def __len__(self, /) -> int:
pass | 3 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 2 | 26 | 3 | 0 | 3 | 3 | 2 | 0 | 5 | 3 | 2 | 1 | 5 | 0 | 2 |
322,613 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/common/_typing.py | sklearn.externals.array_api_compat.common._typing.SupportsArrayNamespace | from typing import TYPE_CHECKING, Literal, Protocol, TypeAlias, TypedDict, TypeVar, final
class SupportsArrayNamespace(Protocol[_T_co]):
def __array_namespace__(self, /, *, api_version: str | None) -> _T_co:
... |
class SupportsArrayNamespace(Protocol[_T_co]):
def __array_namespace__(self, /, *, api_version: str | None) -> _T_co:
pass | 2 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 25 | 2 | 0 | 2 | 2 | 1 | 0 | 3 | 2 | 1 | 1 | 5 | 0 | 1 |
322,614 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/cupy/_info.py | sklearn.externals.array_api_compat.cupy._info.__array_namespace_info__ | from cupy import dtype, cuda, bool_ as bool, intp, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float32, float64, complex64, complex128
class __array_namespace_info__:
"""
Get the array API inspection namespace for CuPy.
The array API inspection namespace defines the following functions:
... |
class __array_namespace_info__:
'''
Get the array API inspection namespace for CuPy.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
https://data-apis.org/array-api/latest/API_s... | 6 | 6 | 54 | 6 | 18 | 29 | 3 | 1.82 | 0 | 4 | 0 | 0 | 5 | 0 | 5 | 5 | 307 | 42 | 94 | 9 | 88 | 171 | 33 | 9 | 27 | 11 | 0 | 2 | 15 |
322,615 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/dask/array/_info.py | sklearn.externals.array_api_compat.dask.array._info.__array_namespace_info__ | from ...common._typing import Capabilities, DefaultDTypes, DType, DTypeKind, DTypesAll, DTypesAny, DTypesBool, DTypesComplex, DTypesIntegral, DTypesNumeric, DTypesReal, DTypesSigned, DTypesUnsigned
from numpy import complex64, complex128, dtype, float32, float64, int8, int16, int32, int64, intp, uint8, uint16, uint32, ... |
class __array_namespace_info__:
'''
Get the array API inspection namespace for Dask.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
https://data-apis.org/array-api/latest/API_s... | 22 | 6 | 24 | 3 | 10 | 12 | 2 | 1.28 | 0 | 14 | 8 | 0 | 13 | 0 | 13 | 13 | 361 | 47 | 138 | 43 | 106 | 177 | 53 | 17 | 39 | 12 | 0 | 2 | 25 |
322,616 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/numpy/_info.py | sklearn.externals.array_api_compat.numpy._info.__array_namespace_info__ | from ._typing import Device, DType
from numpy import bool_ as bool
from numpy import complex64, complex128, dtype, float32, float64, int8, int16, int32, int64, intp, uint8, uint16, uint32, uint64
class __array_namespace_info__:
"""
Get the array API inspection namespace for NumPy.
The array API inspection... |
class __array_namespace_info__:
'''
Get the array API inspection namespace for NumPy.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
https://data-apis.org/array-api/latest/API_... | 6 | 6 | 58 | 6 | 22 | 29 | 3 | 1.5 | 0 | 6 | 0 | 0 | 5 | 0 | 5 | 5 | 327 | 44 | 113 | 18 | 98 | 170 | 37 | 9 | 31 | 12 | 0 | 2 | 17 |
322,617 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_compat/torch/_info.py | sklearn.externals.array_api_compat.torch._info.__array_namespace_info__ | from functools import cache
import torch
class __array_namespace_info__:
"""
Get the array API inspection namespace for PyTorch.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
... |
class __array_namespace_info__:
'''
Get the array API inspection namespace for PyTorch.
The array API inspection namespace defines the following functions:
- capabilities()
- default_device()
- default_dtypes()
- dtypes()
- devices()
See
https://data-apis.org/array-api/latest/AP... | 9 | 6 | 52 | 5 | 20 | 28 | 4 | 1.55 | 0 | 5 | 0 | 0 | 6 | 0 | 6 | 6 | 356 | 46 | 122 | 33 | 113 | 189 | 71 | 30 | 64 | 11 | 0 | 4 | 24 |
322,618 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_extra/_lib/_at.py | sklearn.externals.array_api_extra._lib._at.Undef | from enum import Enum
class Undef(Enum):
"""Sentinel for undefined values."""
UNDEF = 0 |
class Undef(Enum):
'''Sentinel for undefined values.'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 4 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 0 | 4 | 0 | 0 |
322,619 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_extra/_lib/_at.py | sklearn.externals.array_api_extra._lib._at._AtOp | from enum import Enum
class _AtOp(Enum):
"""Operations for use in `xpx.at`."""
SET = 'set'
ADD = 'add'
SUBTRACT = 'subtract'
MULTIPLY = 'multiply'
DIVIDE = 'divide'
POWER = 'power'
MIN = 'min'
MAX = 'max'
def __str__(self) -> str:
"""
Return string representatio... |
class _AtOp(Enum):
'''Operations for use in `xpx.at`.'''
def __str__(self) -> str:
'''
Return string representation (useful for pytest logs).
Returns
-------
str
The operation's name.
'''
pass | 2 | 2 | 10 | 1 | 2 | 8 | 1 | 0.91 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 50 | 23 | 3 | 11 | 10 | 9 | 10 | 11 | 10 | 9 | 1 | 4 | 0 | 1 |
322,620 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_extra/_lib/_at.py | sklearn.externals.array_api_extra._lib._at.at | import operator
from collections.abc import Callable
from typing import TYPE_CHECKING, ClassVar, cast
from types import ModuleType
from ._utils._typing import Array, SetIndex
from ._utils._helpers import meta_namespace
from ._utils._compat import array_namespace, is_dask_array, is_jax_array, is_writeable_array
class a... |
class at:
'''
Update operations for read-only arrays.
This implements ``jax.numpy.ndarray.at`` for all writeable
backends (those that support ``__setitem__``) and routes
to the ``.at[]`` method for JAX arrays.
Parameters
----------
x : array
Input array.
idx : index, optiona... | 12 | 11 | 22 | 2 | 14 | 7 | 2 | 1.19 | 0 | 7 | 2 | 0 | 11 | 0 | 11 | 11 | 396 | 71 | 156 | 82 | 84 | 186 | 73 | 23 | 60 | 14 | 0 | 3 | 27 |
322,621 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/externals/array_api_extra/_lib/_backends.py | sklearn.externals.array_api_extra._lib._backends.Backend | from collections.abc import Callable
from typing import cast
from enum import Enum
from ._utils import _compat
from types import ModuleType
class Backend(Enum):
"""
All array library backends explicitly tested by array-api-extra.
Parameters
----------
value : str
Name of the backend's modu... |
class Backend(Enum):
'''
All array library backends explicitly tested by array-api-extra.
Parameters
----------
value : str
Name of the backend's module.
is_namespace : Callable[[ModuleType], bool]
Function to check whether an input module is the array namespace
correspo... | 4 | 2 | 5 | 0 | 5 | 2 | 1 | 0.7 | 1 | 4 | 0 | 0 | 3 | 1 | 3 | 52 | 39 | 5 | 23 | 20 | 13 | 16 | 17 | 14 | 13 | 1 | 4 | 0 | 3 |
322,622 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/_dict_vectorizer.py | sklearn.feature_extraction._dict_vectorizer.DictVectorizer | import scipy.sparse as sp
import numpy as np
from ..base import BaseEstimator, TransformerMixin, _fit_context
from collections.abc import Iterable, Mapping
from ..utils import check_array
from operator import itemgetter
from sklearn.utils import metadata_routing
from ..utils.validation import check_is_fitted
from numbe... |
class DictVectorizer(TransformerMixin, BaseEstimator):
'''Transforms lists of feature-value mappings to vectors.
This transformer turns lists of mappings (dict-like objects) of feature
names to feature values into Numpy arrays or scipy.sparse matrices for use
with scikit-learn estimators.
When feat... | 13 | 8 | 34 | 5 | 18 | 11 | 5 | 0.94 | 2 | 16 | 1 | 0 | 10 | 6 | 10 | 45 | 441 | 75 | 189 | 64 | 165 | 178 | 131 | 51 | 120 | 16 | 2 | 4 | 46 |
322,623 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/_hash.py | sklearn.feature_extraction._hash.FeatureHasher | from ..utils._param_validation import Interval, StrOptions
from numbers import Integral
from itertools import chain
import numpy as np
from ..base import BaseEstimator, TransformerMixin, _fit_context
import scipy.sparse as sp
from ._hashing_fast import transform as _hashing_transform
from sklearn.utils import metadata_... |
class FeatureHasher(TransformerMixin, BaseEstimator):
'''Implements feature hashing, aka the hashing trick.
This class turns sequences of symbolic feature names (strings) into
scipy.sparse matrices, using a hash function to compute the matrix column
corresponding to a name. The hash function employed i... | 6 | 3 | 22 | 3 | 12 | 7 | 3 | 1.81 | 2 | 5 | 1 | 0 | 4 | 4 | 4 | 39 | 187 | 29 | 57 | 25 | 44 | 103 | 33 | 17 | 28 | 5 | 2 | 2 | 10 |
322,624 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/image.py | sklearn.feature_extraction.image.PatchExtractor | from ..utils import check_array, check_random_state
import numpy as np
from ..utils.validation import validate_data
from ..base import BaseEstimator, TransformerMixin, _fit_context
from numbers import Integral, Number, Real
from ..utils._param_validation import Hidden, Interval, RealNotInt, validate_params
class Patch... |
class PatchExtractor(TransformerMixin, BaseEstimator):
'''Extracts patches from a collection of images.
Read more in the :ref:`User Guide <image_feature_extraction>`.
.. versionadded:: 0.9
Parameters
----------
patch_size : tuple of int (patch_height, patch_width), default=None
The dime... | 6 | 3 | 24 | 2 | 13 | 9 | 2 | 1.24 | 2 | 3 | 0 | 0 | 4 | 3 | 4 | 39 | 163 | 22 | 63 | 20 | 57 | 78 | 35 | 19 | 30 | 5 | 2 | 2 | 8 |
322,625 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/text.py | sklearn.feature_extraction.text.CountVectorizer | from numbers import Integral
from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
from sklearn.utils import metadata_routing
from collections.abc import Mapping
import scipy.sparse as sp
from ..utils.fix... |
class CountVectorizer(_VectorizerMixin, BaseEstimator):
'''Convert a collection of text documents to a matrix of token counts.
This implementation produces a sparse representation of the counts using
scipy.sparse.csr_matrix.
If you do not provide an a-priori dictionary and you do not use an analyzer
... | 12 | 9 | 32 | 4 | 21 | 8 | 4 | 1.02 | 2 | 16 | 1 | 1 | 10 | 18 | 10 | 54 | 555 | 84 | 235 | 90 | 203 | 239 | 151 | 68 | 140 | 12 | 2 | 4 | 41 |
322,626 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/text.py | sklearn.feature_extraction.text.HashingVectorizer | from ..preprocessing import normalize
from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
import numpy as np
from ._hash import FeatureHasher
from numbers import Integral
class HashingVectorizer(Transf... |
class HashingVectorizer(TransformerMixin, _VectorizerMixin, BaseEstimator, auto_wrap_output_keys=None):
'''Convert a collection of text documents to a matrix of token occurrences.
It turns a collection of text documents into a scipy.sparse matrix holding
token occurrence counts (or binary occurrence inform... | 10 | 5 | 21 | 2 | 11 | 8 | 2 | 2.05 | 4 | 4 | 1 | 0 | 7 | 16 | 7 | 55 | 360 | 65 | 97 | 50 | 66 | 199 | 48 | 27 | 40 | 4 | 2 | 1 | 11 |
322,627 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/text.py | sklearn.feature_extraction.text.TfidfTransformer | from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils.fixes import _IS_32BIT
import numpy as np
from ..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted, validate_data
import... |
class TfidfTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
'''Transform a count matrix to a normalized tf or tf-idf representation.
Tf means term-frequency while tf-idf means term-frequency times inverse
document-frequency. This is a common term weighting sch... | 6 | 3 | 25 | 4 | 12 | 10 | 3 | 2.43 | 4 | 4 | 1 | 0 | 4 | 5 | 4 | 40 | 242 | 43 | 58 | 18 | 50 | 141 | 41 | 15 | 36 | 5 | 2 | 1 | 11 |
322,628 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/text.py | sklearn.feature_extraction.text.TfidfVectorizer | from ..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted, validate_data
from ..exceptions import NotFittedError
import numpy as np
import warnings
from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils._param_validation import HasMethods, Interval, RealNotIn... |
class TfidfVectorizer(CountVectorizer):
'''Convert a collection of raw documents to a matrix of TF-IDF features.
Equivalent to :class:`CountVectorizer` followed by
:class:`TfidfTransformer`.
For an example of usage, see
:ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`... | 12 | 5 | 22 | 2 | 14 | 6 | 2 | 1.69 | 1 | 5 | 2 | 0 | 8 | 5 | 8 | 62 | 403 | 64 | 126 | 46 | 90 | 213 | 49 | 19 | 40 | 5 | 3 | 2 | 14 |
322,629 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_extraction/text.py | sklearn.feature_extraction.text._VectorizerMixin | import numpy as np
from ..exceptions import NotFittedError
from collections.abc import Mapping
from functools import partial
import re
import warnings
class _VectorizerMixin:
"""Provides common code for text vectorizers (tokenization logic)."""
_white_spaces = re.compile('\\s\\s+')
def decode(self, doc):
... |
class _VectorizerMixin:
'''Provides common code for text vectorizers (tokenization logic).'''
def decode(self, doc):
'''Decode the input into a string of unicode symbols.
The decoding strategy depends on the vectorizer parameters.
Parameters
----------
doc : bytes or st... | 14 | 12 | 26 | 3 | 18 | 5 | 5 | 0.31 | 0 | 12 | 1 | 2 | 13 | 4 | 13 | 13 | 355 | 54 | 230 | 55 | 216 | 72 | 156 | 53 | 142 | 10 | 0 | 4 | 66 |
322,630 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_base.py | sklearn.feature_selection._base.SelectorMixin | from ..utils._set_output import _get_output_config
from ..utils import _safe_indexing, check_array, safe_sqr
from ..utils.validation import _check_feature_names_in, _is_pandas_df, check_is_fitted, validate_data
from abc import ABCMeta, abstractmethod
from scipy.sparse import csc_matrix, issparse
from ..base import Tran... |
class SelectorMixin(TransformerMixin, metaclass=ABCMeta):
'''
Transformer mixin that performs feature selection given a support mask
This mixin provides a feature selector implementation with `transform` and
`inverse_transform` functionality given an implementation of
`_get_support_mask`.
Examp... | 8 | 7 | 23 | 2 | 9 | 12 | 2 | 1.6 | 2 | 3 | 1 | 6 | 6 | 0 | 6 | 30 | 172 | 21 | 58 | 17 | 50 | 93 | 39 | 16 | 32 | 4 | 3 | 2 | 12 |
322,631 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_from_model.py | sklearn.feature_selection._from_model.SelectFromModel | from ..exceptions import NotFittedError
from numbers import Integral, Real
from copy import deepcopy
import numpy as np
from ..utils._tags import get_tags
from ..utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
from ..utils.metaestimators import available_if
from ..utils.va... |
class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
'''Meta-transformer for selecting features based on importance weights.
.. versionadded:: 0.17
Read more in the :ref:`User Guide <select_from_model>`.
Parameters
----------
estimator : object
The base estimator fro... | 15 | 6 | 27 | 3 | 17 | 7 | 3 | 1.09 | 3 | 8 | 3 | 0 | 9 | 9 | 9 | 70 | 427 | 69 | 172 | 53 | 144 | 187 | 95 | 34 | 85 | 8 | 4 | 3 | 28 |
322,632 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_rfe.py | sklearn.feature_selection._rfe.RFE | from ..utils import Bunch, metadata_routing
from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
from ..utils._metadata_requests import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
import warnings
from ._base import SelectorMixin, _get_feature_i... |
class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
'''Feature ranking with recursive feature elimination.
Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), the goal of recursive feature elimination
(RFE) is to select features by recursive... | 22 | 9 | 25 | 4 | 11 | 10 | 2 | 1.35 | 3 | 9 | 3 | 1 | 13 | 13 | 13 | 74 | 488 | 86 | 172 | 62 | 140 | 232 | 107 | 44 | 93 | 10 | 4 | 2 | 27 |
322,633 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_rfe.py | sklearn.feature_selection._rfe.RFECV | from ..model_selection import check_cv
from ..utils import Bunch, metadata_routing
from ..utils.parallel import Parallel, delayed
from ..utils.validation import _check_method_params, _deprecate_positional_args, _estimator_has, check_is_fitted, validate_data
import warnings
from numbers import Integral
import numpy as n... |
class RFECV(RFE):
'''Recursive feature elimination with cross-validation to select features.
The number of features selected is tuned automatically by fitting an :class:`RFE`
selector on the different cross-validation splits (provided by the `cv` parameter).
The performance of each :class:`RFE` selecto... | 8 | 4 | 48 | 6 | 27 | 15 | 2 | 1.55 | 1 | 8 | 4 | 0 | 5 | 13 | 5 | 79 | 468 | 86 | 150 | 51 | 129 | 232 | 70 | 39 | 64 | 5 | 5 | 2 | 12 |
322,634 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_sequential.py | sklearn.feature_selection._sequential.SequentialFeatureSelector | from ..utils._metadata_requests import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
from ..metrics import check_scoring, get_scorer_names
from ..model_selection import check_cv, cross_val_scor... |
class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
'''Transformer that performs Sequential Feature Selection.
This Sequential Feature Selector adds (forward selection) or
removes (backward selection) features to form a feature subset in a
greedy fashion. At each stage, t... | 8 | 3 | 30 | 3 | 19 | 8 | 3 | 1.17 | 3 | 9 | 2 | 0 | 6 | 9 | 6 | 67 | 336 | 56 | 129 | 48 | 109 | 151 | 70 | 35 | 63 | 12 | 4 | 2 | 19 |
322,635 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection.GenericUnivariateSelect | from numbers import Integral, Real
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
class GenericUnivariateSelect(_BaseFilter):
"""Univariate feature selector with configurable strategy.
Read more in the :ref:`User Guide ... |
class GenericUnivariateSelect(_BaseFilter):
'''Univariate feature selector with configurable strategy.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable, default=f_classif
Function taking two arrays X and y, and returning a pair o... | 6 | 1 | 5 | 1 | 4 | 0 | 1 | 1.51 | 1 | 1 | 0 | 0 | 5 | 2 | 5 | 70 | 110 | 22 | 35 | 14 | 29 | 53 | 25 | 14 | 19 | 1 | 5 | 0 | 5 |
322,636 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection.SelectFdr | import numpy as np
from numbers import Integral, Real
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
class SelectFdr(_BaseFilter):
"""Filter: Select the p-values for an estimated false discovery rate.
This uses the Benj... |
class SelectFdr(_BaseFilter):
'''Filter: Select the p-values for an estimated false discovery rate.
This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound
on the expected false discovery rate.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
-------... | 3 | 1 | 7 | 1 | 7 | 0 | 2 | 3 | 1 | 3 | 0 | 0 | 2 | 2 | 2 | 67 | 89 | 17 | 18 | 9 | 15 | 54 | 13 | 8 | 10 | 2 | 5 | 1 | 3 |
322,637 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection.SelectFpr | from numbers import Integral, Real
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
class SelectFpr(_BaseFilter):
"""Filter: Select the pvalues below alpha based on a FPR test.
FPR test stands for False Positive Rate test... |
class SelectFpr(_BaseFilter):
'''Filter: Select the pvalues below alpha based on a FPR test.
FPR test stands for False Positive Rate test. It controls the total
amount of false detections.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : ... | 3 | 1 | 4 | 1 | 3 | 0 | 1 | 4.64 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 67 | 78 | 16 | 11 | 5 | 8 | 51 | 8 | 5 | 5 | 1 | 5 | 0 | 2 |
322,638 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection.SelectFwe | from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
from numbers import Integral, Real
class SelectFwe(_BaseFilter):
"""Filter: Select the p-values corresponding to Family-wise error rate.
Read more in the :ref:`User Guide <... |
class SelectFwe(_BaseFilter):
'''Filter: Select the p-values corresponding to Family-wise error rate.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable, default=f_classif
Function taking two arrays X and y, and returning a pair of... | 3 | 1 | 4 | 1 | 3 | 0 | 1 | 4.27 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 67 | 73 | 15 | 11 | 5 | 8 | 47 | 8 | 5 | 5 | 1 | 5 | 0 | 2 |
322,639 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection.SelectKBest | from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
import warnings
import numpy as np
from numbers import Integral, Real
class SelectKBest(_BaseFilter):
"""Select features according to the k highest scores.
Read more in the... |
class SelectKBest(_BaseFilter):
'''Select features according to the k highest scores.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays
... | 5 | 1 | 7 | 1 | 6 | 1 | 2 | 2.03 | 1 | 3 | 0 | 0 | 4 | 1 | 4 | 69 | 109 | 21 | 29 | 10 | 24 | 59 | 21 | 10 | 16 | 3 | 5 | 1 | 7 |
322,640 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection.SelectPercentile | from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
import numpy as np
from numbers import Integral, Real
class SelectPercentile(_BaseFilter):
"""Select features according to a percentile of the highest scores.
Read more in ... |
class SelectPercentile(_BaseFilter):
'''Select features according to a percentile of the highest scores.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable, default=f_classif
Function taking two arrays X and y, and returning a pair... | 4 | 1 | 8 | 1 | 7 | 0 | 2 | 2.07 | 1 | 3 | 0 | 0 | 3 | 2 | 3 | 68 | 103 | 20 | 27 | 14 | 23 | 56 | 23 | 13 | 19 | 4 | 5 | 1 | 6 |
322,641 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_univariate_selection.py | sklearn.feature_selection._univariate_selection._BaseFilter | import numpy as np
from ..utils.validation import check_is_fitted, validate_data
from ._base import SelectorMixin
from ..base import BaseEstimator, _fit_context
class _BaseFilter(SelectorMixin, BaseEstimator):
"""Initialize the univariate feature selection.
Parameters
----------
score_func : callable
... |
class _BaseFilter(SelectorMixin, BaseEstimator):
'''Initialize the univariate feature selection.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues) or a single array with scores.
'''
def __init__(self... | 6 | 2 | 11 | 2 | 7 | 3 | 2 | 0.69 | 2 | 3 | 0 | 6 | 4 | 3 | 4 | 65 | 61 | 12 | 29 | 11 | 23 | 20 | 24 | 10 | 19 | 3 | 4 | 1 | 6 |
322,642 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/feature_selection/_variance_threshold.py | sklearn.feature_selection._variance_threshold.VarianceThreshold | import numpy as np
from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
from ..utils._param_validation import Interval
from ..base import BaseEstimator, _fit_context
from ._base import SelectorMixin
from ..utils.validation import check_is_fitted, validate_data
from numbers import Real
class VarianceThresho... |
class VarianceThreshold(SelectorMixin, BaseEstimator):
'''Feature selector that removes all low-variance features.
This feature selection algorithm looks only at the features (X), not the
desired outputs (y), and can thus be used for unsupervised learning.
Read more in the :ref:`User Guide <variance_th... | 6 | 2 | 15 | 2 | 9 | 4 | 3 | 1.51 | 2 | 2 | 0 | 0 | 4 | 2 | 4 | 65 | 127 | 25 | 41 | 14 | 35 | 62 | 31 | 13 | 26 | 7 | 4 | 2 | 10 |
322,643 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/frozen/_frozen.py | sklearn.frozen._frozen.FrozenEstimator | from copy import deepcopy
from ..base import BaseEstimator
from ..utils.metaestimators import available_if
from ..utils.validation import check_is_fitted
from ..utils import get_tags
from ..exceptions import NotFittedError
class FrozenEstimator(BaseEstimator):
"""Estimator that wraps a fitted estimator to prevent ... |
class FrozenEstimator(BaseEstimator):
'''Estimator that wraps a fitted estimator to prevent re-fitting.
This meta-estimator takes an estimator and freezes it, in the sense that calling
`fit` on it has no effect. `fit_predict` and `fit_transform` are also disabled.
All other methods are delegated to the... | 11 | 5 | 11 | 1 | 4 | 5 | 1 | 2 | 1 | 3 | 1 | 0 | 9 | 1 | 9 | 40 | 140 | 26 | 38 | 14 | 27 | 76 | 32 | 13 | 22 | 3 | 2 | 1 | 13 |
322,644 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/_gpc.py | sklearn.gaussian_process._gpc.GaussianProcessClassifier | from ..base import BaseEstimator, ClassifierMixin, _fit_context, clone
from ..utils.validation import check_is_fitted, validate_data
from numbers import Integral
import numpy as np
from ..utils._param_validation import Interval, StrOptions
from ..multiclass import OneVsOneClassifier, OneVsRestClassifier
from .kernels i... |
class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):
'''Gaussian process classification (GPC) based on Laplace approximation.
The implementation is based on Algorithm 3.1, 3.2, and 5.1 from [RW2006]_.
Internally, the Laplace approximation is used for approximating the
non-Gaussian posterior... | 10 | 7 | 39 | 5 | 23 | 12 | 4 | 1.23 | 2 | 7 | 4 | 0 | 7 | 13 | 7 | 40 | 458 | 71 | 174 | 41 | 150 | 214 | 76 | 24 | 68 | 8 | 2 | 2 | 26 |
322,645 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/_gpc.py | sklearn.gaussian_process._gpc._BinaryGaussianProcessClassifierLaplace | from operator import itemgetter
from scipy.special import erf, expit
import numpy as np
from ..preprocessing import LabelEncoder
import scipy.optimize
from ..utils.optimize import _check_optimize_result
from .kernels import ConstantKernel as C
from ..utils import check_random_state
from .kernels import RBF, CompoundKer... |
class _BinaryGaussianProcessClassifierLaplace(BaseEstimator):
'''Binary Gaussian process classification based on Laplace approximation.
The implementation is based on Algorithm 3.1, 3.2, and 5.1 from [RW2006]_.
Internally, the Laplace approximation is used for approximating the
non-Gaussian posterior b... | 10 | 7 | 40 | 5 | 21 | 15 | 3 | 1.24 | 1 | 7 | 2 | 0 | 8 | 17 | 8 | 39 | 478 | 80 | 184 | 80 | 162 | 228 | 120 | 68 | 110 | 9 | 2 | 3 | 30 |
322,646 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/_gpr.py | sklearn.gaussian_process._gpr.GaussianProcessRegressor | from scipy.linalg import cho_solve, cholesky, solve_triangular
from .kernels import ConstantKernel as C
from numbers import Integral, Real
import numpy as np
from ..utils import check_random_state
from ..base import BaseEstimator, MultiOutputMixin, RegressorMixin, _fit_context, clone
from ..utils.optimize import _check... |
class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
'''Gaussian process regression (GPR).
The implementation is based on Algorithm 2.1 of [RW2006]_.
In addition to standard scikit-learn estimator API,
:class:`GaussianProcessRegressor`:
* allows prediction without prior ... | 10 | 5 | 59 | 7 | 34 | 19 | 6 | 1.03 | 3 | 8 | 1 | 0 | 7 | 17 | 7 | 41 | 650 | 96 | 275 | 78 | 252 | 282 | 164 | 63 | 155 | 16 | 2 | 3 | 51 |
322,647 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.CompoundKernel | import numpy as np
class CompoundKernel(Kernel):
"""Kernel which is composed of a set of other kernels.
.. versionadded:: 0.18
Parameters
----------
kernels : list of Kernels
The other kernels
Examples
--------
>>> from sklearn.gaussian_process.kernels import WhiteKernel
... |
class CompoundKernel(Kernel):
'''Kernel which is composed of a set of other kernels.
.. versionadded:: 0.18
Parameters
----------
kernels : list of Kernels
The other kernels
Examples
--------
>>> from sklearn.gaussian_process.kernels import WhiteKernel
>>> from sklearn.gauss... | 15 | 9 | 12 | 2 | 4 | 7 | 1 | 2.35 | 1 | 3 | 0 | 0 | 10 | 1 | 10 | 50 | 162 | 28 | 40 | 22 | 25 | 94 | 33 | 18 | 22 | 3 | 4 | 2 | 14 |
322,648 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.ConstantKernel | from ..utils.validation import _num_samples
import numpy as np
class ConstantKernel(StationaryKernelMixin, GenericKernelMixin, Kernel):
"""Constant kernel.
Can be used as part of a product-kernel where it scales the magnitude of
the other factor (kernel) or as part of a sum-kernel, where it modifies
t... |
class ConstantKernel(StationaryKernelMixin, GenericKernelMixin, Kernel):
'''Constant kernel.
Can be used as part of a product-kernel where it scales the magnitude of
the other factor (kernel) or as part of a sum-kernel, where it modifies
the mean of the Gaussian process.
.. math::
k(x_1, x_... | 7 | 3 | 16 | 2 | 7 | 7 | 2 | 1.82 | 3 | 2 | 1 | 1 | 5 | 2 | 5 | 47 | 136 | 26 | 39 | 10 | 32 | 71 | 20 | 9 | 14 | 5 | 4 | 2 | 9 |
322,649 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.DotProduct | import numpy as np
class DotProduct(Kernel):
"""Dot-Product kernel.
The DotProduct kernel is non-stationary and can be obtained from linear
regression by putting :math:`N(0, 1)` priors on the coefficients
of :math:`x_d (d = 1, . . . , D)` and a prior of :math:`N(0, \\sigma_0^2)`
on the bias. The D... |
class DotProduct(Kernel):
'''Dot-Product kernel.
The DotProduct kernel is non-stationary and can be obtained from linear
regression by putting :math:`N(0, 1)` priors on the coefficients
of :math:`x_d (d = 1, . . . , D)` and a prior of :math:`N(0, \sigma_0^2)`
on the bias. The DotProduct kernel is i... | 8 | 4 | 12 | 2 | 5 | 6 | 2 | 2.7 | 1 | 2 | 1 | 0 | 6 | 2 | 6 | 46 | 136 | 25 | 30 | 12 | 22 | 81 | 26 | 11 | 19 | 5 | 4 | 2 | 10 |
322,650 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.ExpSineSquared | import numpy as np
from scipy.spatial.distance import cdist, pdist, squareform
class ExpSineSquared(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
"""Exp-Sine-Squared kernel (aka periodic kernel).
The ExpSineSquared kernel allows one to model functions which repeat
themselves exactly. It is parame... |
class ExpSineSquared(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
'''Exp-Sine-Squared kernel (aka periodic kernel).
The ExpSineSquared kernel allows one to model functions which repeat
themselves exactly. It is parameterized by a length scale
parameter :math:`l>0` and a periodicity parameter ... | 8 | 3 | 16 | 1 | 10 | 5 | 2 | 1.3 | 3 | 2 | 1 | 0 | 5 | 4 | 5 | 47 | 143 | 23 | 53 | 25 | 39 | 69 | 35 | 17 | 29 | 6 | 4 | 2 | 10 |
322,651 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.Exponentiation | import numpy as np
class Exponentiation(Kernel):
"""The Exponentiation kernel takes one base kernel and a scalar parameter
:math:`p` and combines them via
.. math::
k_{exp}(X, Y) = k(X, Y) ^p
Note that the `__pow__` magic method is overridden, so
`Exponentiation(RBF(), 2)` is equivalent t... |
class Exponentiation(Kernel):
'''The Exponentiation kernel takes one base kernel and a scalar parameter
:math:`p` and combines them via
.. math::
k_{exp}(X, Y) = k(X, Y) ^p
Note that the `__pow__` magic method is overridden, so
`Exponentiation(RBF(), 2)` is equivalent to using the ** operat... | 18 | 10 | 11 | 1 | 4 | 6 | 1 | 1.91 | 1 | 2 | 1 | 0 | 12 | 2 | 12 | 52 | 189 | 35 | 53 | 25 | 35 | 101 | 40 | 20 | 27 | 2 | 4 | 1 | 16 |
322,652 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.GenericKernelMixin | class GenericKernelMixin:
"""Mixin for kernels which operate on generic objects such as variable-
length sequences, trees, and graphs.
.. versionadded:: 0.22
"""
@property
def requires_vector_input(self):
"""Whether the kernel works only on fixed-length feature vectors."""
retu... | class GenericKernelMixin:
'''Mixin for kernels which operate on generic objects such as variable-
length sequences, trees, and graphs.
.. versionadded:: 0.22
'''
@property
def requires_vector_input(self):
'''Whether the kernel works only on fixed-length feature vectors.'''
pass | 3 | 2 | 3 | 0 | 2 | 1 | 1 | 1.25 | 0 | 0 | 0 | 3 | 1 | 0 | 1 | 1 | 11 | 2 | 4 | 3 | 1 | 5 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
322,653 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.Hyperparameter | import numpy as np
from collections import namedtuple
class Hyperparameter(namedtuple('Hyperparameter', ('name', 'value_type', 'bounds', 'n_elements', 'fixed'))):
"""A kernel hyperparameter's specification in form of a namedtuple.
.. versionadded:: 0.18
Attributes
----------
name : str
Th... |
class Hyperparameter(namedtuple('Hyperparameter', ('name', 'value_type', 'bounds', 'n_elements', 'fixed'))):
'''A kernel hyperparameter's specification in form of a namedtuple.
.. versionadded:: 0.18
Attributes
----------
name : str
The name of the hyperparameter. Note that a kernel using a... | 3 | 1 | 12 | 1 | 12 | 1 | 4 | 1.9 | 1 | 3 | 0 | 0 | 2 | 0 | 2 | 7 | 97 | 14 | 29 | 8 | 22 | 55 | 14 | 4 | 11 | 6 | 1 | 3 | 7 |
322,654 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.Kernel | from ..base import clone
from inspect import signature
from abc import ABCMeta, abstractmethod
import warnings
from ..exceptions import ConvergenceWarning
import numpy as np
class Kernel(metaclass=ABCMeta):
"""Base class for all kernels.
.. versionadded:: 0.18
Examples
--------
>>> from sklearn.g... |
class Kernel(metaclass=ABCMeta):
'''Base class for all kernels.
.. versionadded:: 0.18
Examples
--------
>>> from sklearn.gaussian_process.kernels import Kernel, RBF
>>> import numpy as np
>>> class CustomKernel(Kernel):
... def __init__(self, length_scale=1.0):
... self... | 30 | 14 | 13 | 1 | 8 | 4 | 3 | 0.56 | 1 | 12 | 5 | 11 | 20 | 0 | 20 | 40 | 309 | 38 | 174 | 58 | 144 | 97 | 115 | 49 | 94 | 6 | 3 | 3 | 50 |
322,655 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.KernelOperator | import numpy as np
class KernelOperator(Kernel):
"""Base class for all kernel operators.
.. versionadded:: 0.18
"""
def __init__(self, k1, k2):
self.k1 = k1
self.k2 = k2
def get_params(self, deep=True):
"""Get parameters of this kernel.
Parameters
-------... |
class KernelOperator(Kernel):
'''Base class for all kernel operators.
.. versionadded:: 0.18
'''
def __init__(self, k1, k2):
pass
def get_params(self, deep=True):
'''Get parameters of this kernel.
Parameters
----------
deep : bool, default=True
... | 15 | 8 | 11 | 1 | 6 | 4 | 2 | 0.66 | 1 | 2 | 1 | 2 | 9 | 2 | 9 | 49 | 116 | 18 | 59 | 21 | 44 | 39 | 37 | 16 | 27 | 3 | 4 | 1 | 14 |
322,656 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.Matern | import numpy as np
import math
from scipy.spatial.distance import cdist, pdist, squareform
from scipy.special import gamma, kv
class Matern(RBF):
"""Matern kernel.
The class of Matern kernels is a generalization of the :class:`RBF`.
It has an additional parameter :math:`\\nu` which controls the
smooth... |
class Matern(RBF):
'''Matern kernel.
The class of Matern kernels is a generalization of the :class:`RBF`.
It has an additional parameter :math:`\nu` which controls the
smoothness of the resulting function. The smaller :math:`\nu`,
the less smooth the approximated function is.
As :math:`\nu\righ... | 5 | 2 | 30 | 3 | 20 | 8 | 5 | 1.19 | 1 | 5 | 0 | 0 | 3 | 1 | 3 | 50 | 198 | 28 | 79 | 14 | 74 | 94 | 55 | 14 | 50 | 16 | 5 | 2 | 20 |
322,657 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.NormalizedKernelMixin | import numpy as np
class NormalizedKernelMixin:
"""Mixin for kernels which are normalized: k(X, X)=1.
.. versionadded:: 0.18
"""
def diag(self, X):
"""Returns the diagonal of the kernel k(X, X).
The result of this method is identical to np.diag(self(X)); however,
it can be ev... |
class NormalizedKernelMixin:
'''Mixin for kernels which are normalized: k(X, X)=1.
.. versionadded:: 0.18
'''
def diag(self, X):
'''Returns the diagonal of the kernel k(X, X).
The result of this method is identical to np.diag(self(X)); however,
it can be evaluated more efficien... | 2 | 2 | 18 | 3 | 2 | 13 | 1 | 5.33 | 0 | 0 | 0 | 3 | 1 | 0 | 1 | 1 | 24 | 5 | 3 | 2 | 1 | 16 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
322,658 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.PairwiseKernel | from ..metrics.pairwise import pairwise_kernels
from scipy.special import gamma, kv
import numpy as np
class PairwiseKernel(Kernel):
"""Wrapper for kernels in sklearn.metrics.pairwise.
A thin wrapper around the functionality of the kernels in
sklearn.metrics.pairwise.
Note: Evaluation of eval_gradien... |
class PairwiseKernel(Kernel):
'''Wrapper for kernels in sklearn.metrics.pairwise.
A thin wrapper around the functionality of the kernels in
sklearn.metrics.pairwise.
Note: Evaluation of eval_gradient is not analytic but numeric and all
kernels support only isotropic distances. The parameter g... | 9 | 4 | 15 | 1 | 8 | 6 | 1 | 1.65 | 1 | 1 | 1 | 0 | 6 | 4 | 6 | 46 | 161 | 24 | 52 | 21 | 37 | 86 | 27 | 14 | 19 | 4 | 4 | 2 | 10 |
322,659 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.Product | import numpy as np
class Product(KernelOperator):
"""The `Product` kernel takes two kernels :math:`k_1` and :math:`k_2`
and combines them via
.. math::
k_{prod}(X, Y) = k_1(X, Y) * k_2(X, Y)
Note that the `__mul__` magic method is overridden, so
`Product(RBF(), RBF())` is equivalent to us... |
class Product(KernelOperator):
'''The `Product` kernel takes two kernels :math:`k_1` and :math:`k_2`
and combines them via
.. math::
k_{prod}(X, Y) = k_1(X, Y) * k_2(X, Y)
Note that the `__mul__` magic method is overridden, so
`Product(RBF(), RBF())` is equivalent to using the * operator
... | 4 | 3 | 19 | 3 | 4 | 12 | 1 | 4.64 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 52 | 98 | 19 | 14 | 6 | 10 | 65 | 11 | 6 | 7 | 2 | 5 | 1 | 4 |
322,660 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.RBF | from scipy.spatial.distance import cdist, pdist, squareform
import numpy as np
class RBF(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
"""Radial basis function kernel (aka squared-exponential kernel).
The RBF kernel is a stationary kernel. It is also known as the
"squared exponential" kernel. It ... |
class RBF(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
'''Radial basis function kernel (aka squared-exponential kernel).
The RBF kernel is a stationary kernel. It is also known as the
"squared exponential" kernel. It is parameterized by a length scale
parameter :math:`l>0`, which can either b... | 8 | 2 | 16 | 1 | 10 | 5 | 3 | 1.41 | 3 | 3 | 1 | 1 | 5 | 2 | 5 | 47 | 151 | 22 | 54 | 14 | 46 | 76 | 35 | 12 | 29 | 7 | 4 | 2 | 13 |
322,661 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.RationalQuadratic | from scipy.spatial.distance import cdist, pdist, squareform
import numpy as np
class RationalQuadratic(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
"""Rational Quadratic kernel.
The RationalQuadratic kernel can be seen as a scale mixture (an infinite
sum) of RBF kernels with different characteri... |
class RationalQuadratic(StationaryKernelMixin, NormalizedKernelMixin, Kernel):
'''Rational Quadratic kernel.
The RationalQuadratic kernel can be seen as a scale mixture (an infinite
sum) of RBF kernels with different characteristic length scales. It is
parameterized by a length scale parameter :math:`l... | 8 | 2 | 17 | 2 | 11 | 5 | 2 | 1.32 | 3 | 3 | 1 | 0 | 5 | 4 | 5 | 47 | 154 | 24 | 57 | 24 | 43 | 75 | 37 | 16 | 31 | 7 | 4 | 2 | 11 |
322,662 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.StationaryKernelMixin | class StationaryKernelMixin:
"""Mixin for kernels which are stationary: k(X, Y)= f(X-Y).
.. versionadded:: 0.18
"""
def is_stationary(self):
"""Returns whether the kernel is stationary."""
return True | class StationaryKernelMixin:
'''Mixin for kernels which are stationary: k(X, Y)= f(X-Y).
.. versionadded:: 0.18
'''
def is_stationary(self):
'''Returns whether the kernel is stationary.'''
pass | 2 | 2 | 3 | 0 | 2 | 1 | 1 | 1.33 | 0 | 0 | 0 | 6 | 1 | 0 | 1 | 1 | 9 | 2 | 3 | 2 | 1 | 4 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
322,663 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.Sum | import numpy as np
class Sum(KernelOperator):
"""The `Sum` kernel takes two kernels :math:`k_1` and :math:`k_2`
and combines them via
.. math::
k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)
Note that the `__add__` magic method is overridden, so
`Sum(RBF(), RBF())` is equivalent to using the + ope... |
class Sum(KernelOperator):
'''The `Sum` kernel takes two kernels :math:`k_1` and :math:`k_2`
and combines them via
.. math::
k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)
Note that the `__add__` magic method is overridden, so
`Sum(RBF(), RBF())` is equivalent to using the + operator
with `RBF()... | 4 | 3 | 18 | 3 | 4 | 12 | 1 | 5.33 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 52 | 95 | 19 | 12 | 6 | 8 | 64 | 11 | 6 | 7 | 2 | 5 | 1 | 4 |
322,664 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/gaussian_process/kernels.py | sklearn.gaussian_process.kernels.WhiteKernel | from ..utils.validation import _num_samples
import numpy as np
class WhiteKernel(StationaryKernelMixin, GenericKernelMixin, Kernel):
"""White kernel.
The main use-case of this kernel is as part of a sum-kernel where it
explains the noise of the signal as independently and identically
normally-distribu... |
class WhiteKernel(StationaryKernelMixin, GenericKernelMixin, Kernel):
'''White kernel.
The main use-case of this kernel is as part of a sum-kernel where it
explains the noise of the signal as independently and identically
normally-distributed. The parameter noise_level equals the variance of this
n... | 7 | 3 | 15 | 2 | 6 | 7 | 2 | 2.09 | 3 | 2 | 1 | 0 | 5 | 2 | 5 | 47 | 121 | 22 | 32 | 10 | 25 | 67 | 21 | 9 | 15 | 5 | 4 | 3 | 9 |
322,665 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/impute/_base.py | sklearn.impute._base.MissingIndicator | from scipy import sparse as sp
from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils._missing import is_pandas_na, is_scalar_nan
from ..utils._mask import _get_mask
from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, _check_n_features, check_is_fitted, validate_data
from ..util... |
class MissingIndicator(TransformerMixin, BaseEstimator):
'''Binary indicators for missing values.
Note that this component typically should not be used in a vanilla
:class:`~sklearn.pipeline.Pipeline` consisting of transformers and a
classifier, but rather could be added using a
:class:`~sklearn.pi... | 12 | 7 | 29 | 4 | 14 | 10 | 3 | 1.17 | 2 | 3 | 0 | 0 | 9 | 7 | 9 | 44 | 363 | 64 | 138 | 37 | 119 | 161 | 87 | 28 | 77 | 10 | 2 | 2 | 30 |
322,666 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/impute/_base.py | sklearn.impute._base.SimpleImputer | from ..utils._param_validation import MissingValues, StrOptions
import numpy as np
from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, _check_n_features, check_is_fitted, validate_data
import numpy.ma as ma
import warnings
from ..utils._... |
class SimpleImputer(_BaseImputer):
'''Univariate imputer for completing missing values with simple strategies.
Replace missing values using a descriptive statistic (e.g. mean, median, or
most frequent) along each column, or using a constant value.
Read more in the :ref:`User Guide <impute>`.
.. ver... | 11 | 7 | 52 | 7 | 33 | 12 | 6 | 0.7 | 1 | 12 | 0 | 0 | 9 | 5 | 9 | 50 | 621 | 99 | 308 | 80 | 288 | 215 | 182 | 68 | 172 | 15 | 3 | 4 | 58 |
322,667 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/impute/_base.py | sklearn.impute._base._BaseImputer | from scipy import sparse as sp
from ..utils._missing import is_pandas_na, is_scalar_nan
from functools import partial
import numpy as np
from ..utils._param_validation import MissingValues, StrOptions
from ..base import BaseEstimator, TransformerMixin, _fit_context
class _BaseImputer(TransformerMixin, BaseEstimator):
... |
class _BaseImputer(TransformerMixin, BaseEstimator):
'''Base class for all imputers.
It adds automatically support for `add_indicator`.
'''
def __init__(self, *, missing_values=np.nan, add_indicator=False, keep_empty_features=False):
pass
def _fit_indicator(self, X):
'''Fit a Miss... | 7 | 4 | 10 | 1 | 7 | 1 | 2 | 0.22 | 2 | 4 | 1 | 7 | 6 | 4 | 6 | 41 | 74 | 13 | 50 | 17 | 41 | 11 | 34 | 15 | 27 | 4 | 2 | 2 | 13 |
322,668 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/impute/_iterative.py | sklearn.impute._iterative.IterativeImputer | from numbers import Integral, Real
from ..utils import _safe_indexing, check_array, check_random_state
from ._base import SimpleImputer, _BaseImputer, _check_inputs_dtype
from ..utils._param_validation import HasMethods, Interval, StrOptions
from ..exceptions import ConvergenceWarning
from ..utils.validation import FLO... |
class IterativeImputer(_BaseImputer):
'''Multivariate imputer that estimates each feature from all the others.
A strategy for imputing missing values by modeling each feature with
missing values as a function of other features in a round-robin fashion.
Read more in the :ref:`User Guide <iterative_imput... | 15 | 12 | 58 | 8 | 31 | 20 | 5 | 1.09 | 1 | 12 | 5 | 0 | 11 | 24 | 12 | 53 | 971 | 154 | 391 | 127 | 343 | 427 | 215 | 91 | 201 | 16 | 3 | 4 | 57 |
322,669 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/impute/_knn.py | sklearn.impute._knn.KNNImputer | from ..utils._mask import _get_mask
from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted, validate_data
from ..utils._param_validation import Hidden, Interval, StrOptions
from ._base import _BaseImputer
from ..utils._missing import is_scalar_nan
import numpy as np
from ..metrics import ... |
class KNNImputer(_BaseImputer):
'''Imputation for completing missing values using k-Nearest Neighbors.
Each sample's missing values are imputed using the mean value from
`n_neighbors` nearest neighbors found in the training set. Two samples are
close if the features that neither is missing are close.
... | 8 | 5 | 53 | 9 | 29 | 15 | 3 | 1.11 | 1 | 3 | 0 | 0 | 5 | 8 | 5 | 46 | 388 | 72 | 150 | 56 | 132 | 166 | 86 | 44 | 79 | 6 | 3 | 3 | 18 |
322,670 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/inspection/_plot/decision_boundary.py | sklearn.inspection._plot.decision_boundary.DecisionBoundaryDisplay | from ...preprocessing import LabelEncoder
import numpy as np
from ...utils._response import _get_response_values
from ...utils import _safe_indexing
from ...utils.validation import _is_arraylike_not_scalar, _is_pandas_df, _is_polars_df, _num_features, check_is_fitted
import warnings
from ...base import is_regressor
fro... |
class DecisionBoundaryDisplay:
'''Decisions boundary visualization.
It is recommended to use
:func:`~sklearn.inspection.DecisionBoundaryDisplay.from_estimator`
to create a :class:`DecisionBoundaryDisplay`. All parameters are stored as
attributes.
Read more in the :ref:`User Guide <visualization... | 5 | 3 | 132 | 18 | 70 | 44 | 14 | 1 | 0 | 5 | 1 | 1 | 2 | 10 | 3 | 3 | 502 | 76 | 213 | 60 | 188 | 214 | 111 | 40 | 104 | 22 | 0 | 4 | 42 |
322,671 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/inspection/_plot/partial_dependence.py | sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay | from math import ceil
from ...utils.parallel import Parallel, delayed
from ...utils import Bunch, _safe_indexing, check_array, check_random_state
from itertools import chain
from ...utils._plotting import _validate_style_kwargs
from ...base import is_regressor
import numbers
from ...utils._optional_dependencies import ... |
class PartialDependenceDisplay:
'''Partial Dependence Plot (PDP) and Individual Conditional Expectation (ICE).
It is recommended to use
:func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` to create a
:class:`~sklearn.inspection.PartialDependenceDisplay`. All parameters are stored
as... | 10 | 8 | 158 | 18 | 86 | 55 | 12 | 0.86 | 0 | 19 | 4 | 1 | 7 | 18 | 8 | 8 | 1,469 | 191 | 689 | 221 | 575 | 593 | 320 | 121 | 305 | 39 | 0 | 3 | 99 |
322,672 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/isotonic.py | sklearn.isotonic.IsotonicRegression | from numbers import Real
from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
from .base import BaseEstimator, RegressorMixin, TransformerMixin, _fit_context
from .utils._param_validation import Interval, StrOptions, validate_params
from .utils.validation import _check_sample_weight, check_is_fi... |
class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
'''Isotonic regression model.
Read more in the :ref:`User Guide <isotonic>`.
.. versionadded:: 0.13
Parameters
----------
y_min : float, default=None
Lower bound on the lowest predicted value (the minimum value m... | 14 | 10 | 18 | 3 | 8 | 7 | 2 | 1.57 | 3 | 5 | 0 | 0 | 12 | 10 | 12 | 49 | 335 | 67 | 105 | 37 | 91 | 165 | 80 | 36 | 67 | 3 | 2 | 1 | 19 |
322,673 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/kernel_approximation.py | sklearn.kernel_approximation.AdditiveChi2Sampler | from numbers import Integral, Real
import numpy as np
from .utils.validation import _check_feature_names_in, check_is_fitted, validate_data
import scipy.sparse as sp
from .utils._param_validation import Interval, StrOptions
from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context... |
class AdditiveChi2Sampler(TransformerMixin, BaseEstimator):
'''Approximate feature map for additive chi2 kernel.
Uses sampling the fourier transform of the kernel characteristic
at regular intervals.
Since the kernel that is to be approximated is additive, the components of
the input vectors can be... | 11 | 4 | 24 | 4 | 13 | 7 | 2 | 1.11 | 2 | 5 | 1 | 0 | 5 | 2 | 7 | 42 | 260 | 53 | 98 | 40 | 87 | 109 | 72 | 37 | 64 | 6 | 2 | 2 | 16 |
322,674 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/kernel_approximation.py | sklearn.kernel_approximation.Nystroem | import numpy as np
from .utils._param_validation import Interval, StrOptions
from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
from .utils.validation import _check_feature_names_in, check_is_fitted, validate_data
import warnings
from .utils import check_random_state
from .... |
class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Approximate a kernel map using a subset of the training data.
Constructs an approximate feature map for an arbitrary kernel
using a subset of the data as basis.
Read more in the :ref:`User Guide <nystroem_kernel_approx... | 7 | 3 | 27 | 3 | 18 | 6 | 2 | 1.16 | 3 | 2 | 0 | 0 | 5 | 12 | 5 | 41 | 268 | 43 | 104 | 44 | 86 | 121 | 53 | 32 | 47 | 6 | 2 | 3 | 11 |
322,675 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/kernel_approximation.py | sklearn.kernel_approximation.PolynomialCountSketch | from .utils._param_validation import Interval, StrOptions
import numpy as np
from .utils import check_random_state
from scipy.fft import fft, ifft
from numbers import Integral, Real
from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
from .utils.validation import _check_feat... |
class PolynomialCountSketch(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Polynomial kernel approximation via Tensor Sketch.
Implements Tensor Sketch, which approximates the feature map
of the polynomial kernel::
K(X, Y) = (gamma * <X, Y> + coef0)^degree
by efficiently c... | 6 | 3 | 27 | 5 | 15 | 7 | 3 | 1.35 | 3 | 3 | 0 | 0 | 4 | 8 | 4 | 40 | 211 | 42 | 72 | 31 | 62 | 97 | 47 | 26 | 42 | 9 | 2 | 3 | 13 |
322,676 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/kernel_approximation.py | sklearn.kernel_approximation.RBFSampler | from .utils.extmath import safe_sparse_dot
from .utils.validation import _check_feature_names_in, check_is_fitted, validate_data
from numbers import Integral, Real
from .utils import check_random_state
from .utils._param_validation import Interval, StrOptions
import scipy.sparse as sp
import numpy as np
from .base impo... |
class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Approximate a RBF kernel feature map using random Fourier features.
It implements a variant of Random Kitchen Sinks.[1]
Read more in the :ref:`User Guide <rbf_kernel_approx>`.
Parameters
----------
gamma : 's... | 6 | 3 | 19 | 3 | 9 | 7 | 2 | 2.04 | 3 | 1 | 0 | 0 | 4 | 7 | 4 | 40 | 171 | 31 | 46 | 20 | 40 | 94 | 35 | 19 | 30 | 5 | 2 | 1 | 8 |
322,677 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/kernel_approximation.py | sklearn.kernel_approximation.SkewedChi2Sampler | from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
from .utils.validation import _check_feature_names_in, check_is_fitted, validate_data
from .utils._param_validation import Interval, StrOptions
from .utils.extmath import safe_sparse_dot
from .utils import check_random_stat... |
class SkewedChi2Sampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Approximate feature map for "skewed chi-squared" kernel.
Read more in the :ref:`User Guide <skewed_chi_kernel_approx>`.
Parameters
----------
skewedness : float, default=1.0
"skewedness" parameter ... | 6 | 3 | 18 | 2 | 9 | 8 | 2 | 2.05 | 3 | 2 | 0 | 0 | 4 | 6 | 4 | 40 | 158 | 27 | 43 | 20 | 35 | 88 | 34 | 17 | 29 | 2 | 2 | 1 | 6 |
322,678 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/kernel_ridge.py | sklearn.kernel_ridge.KernelRidge | import numpy as np
from .metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels
from .base import BaseEstimator, MultiOutputMixin, RegressorMixin, _fit_context
from .linear_model._ridge import _solve_cholesky_kernel
from .utils.validation import _check_sample_weight, check_is_fitted, validate_data
from num... |
class KernelRidge(MultiOutputMixin, RegressorMixin, BaseEstimator):
'''Kernel ridge regression.
Kernel ridge regression (KRR) combines ridge regression (linear least
squares with l2-norm regularization) with the kernel trick. It thus
learns a linear function in the space induced by the respective kerne... | 7 | 3 | 18 | 2 | 10 | 6 | 2 | 1.98 | 3 | 2 | 0 | 0 | 5 | 8 | 5 | 39 | 224 | 36 | 63 | 32 | 47 | 125 | 40 | 22 | 34 | 4 | 2 | 1 | 9 |
322,679 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_base.py | sklearn.linear_model._base.LinearClassifierMixin | import numpy as np
from ..utils.extmath import safe_sparse_dot
from scipy.special import expit
from ..utils._array_api import _asarray_with_order, _average, get_namespace, get_namespace_and_device, indexing_dtype, supported_float_dtypes
from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin... |
class LinearClassifierMixin(ClassifierMixin):
'''Mixin for linear classifiers.
Handles prediction for sparse and dense X.
'''
def decision_function(self, X):
'''
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed
dista... | 4 | 4 | 22 | 3 | 9 | 11 | 2 | 1.3 | 1 | 0 | 0 | 7 | 3 | 1 | 3 | 5 | 74 | 12 | 27 | 11 | 23 | 35 | 21 | 10 | 17 | 2 | 1 | 1 | 6 |
322,680 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_base.py | sklearn.linear_model._base.LinearModel | from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data
from ..utils._array_api import _asarray_with_order, _average, get_namespace, get_namespace_and_device, indexing_dtype, supported_float_dtypes
from abc import ABCMeta, abstractmethod
from ..base import BaseEstimator, ClassifierMixin, Mul... |
class LinearModel(BaseEstimator, metaclass=ABCMeta):
'''Base class for Linear Models'''
@abstractmethod
def fit(self, X, y):
'''Fit model.'''
pass
def _decision_function(self, X):
pass
def predict(self, X):
'''
Predict using the linear model.
Parame... | 6 | 4 | 12 | 2 | 6 | 4 | 2 | 0.64 | 2 | 0 | 0 | 15 | 4 | 2 | 4 | 55 | 53 | 12 | 25 | 11 | 19 | 16 | 21 | 10 | 16 | 3 | 3 | 2 | 7 |
322,681 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_base.py | sklearn.linear_model._base.LinearRegression | from scipy import linalg, optimize, sparse
from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data
import numpy as np
from scipy.sparse.linalg import lsqr
import scipy.sparse as sp
from ..utils.parallel import Parallel, delayed
from numbers import Integral, Real
from ..utils._param_validatio... |
class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
'''
Ordinary least squares Linear Regression.
LinearRegression fits a linear model with coefficients w = (w1, ..., wp)
to minimize the residual sum of squares between the observed targets in
the dataset, and the targets predicte... | 9 | 2 | 20 | 3 | 14 | 4 | 2 | 1.16 | 3 | 4 | 1 | 2 | 3 | 8 | 3 | 61 | 255 | 45 | 97 | 36 | 80 | 113 | 50 | 27 | 42 | 10 | 4 | 2 | 16 |
322,682 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_base.py | sklearn.linear_model._base.SparseCoefMixin | import scipy.sparse as sp
from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data
class SparseCoefMixin:
"""Mixin for converting coef_ to and from CSR format.
L1-regularizing estimators should inherit this.
"""
def densify(self):
"""
Convert coefficient mat... |
class SparseCoefMixin:
'''Mixin for converting coef_ to and from CSR format.
L1-regularizing estimators should inherit this.
'''
def densify(self):
'''
Convert coefficient matrix to dense array format.
Converts the ``coef_`` member (back) to a numpy.ndarray. This is the
... | 3 | 3 | 25 | 4 | 6 | 16 | 2 | 2.83 | 0 | 1 | 1 | 3 | 2 | 1 | 2 | 2 | 56 | 10 | 12 | 6 | 9 | 34 | 12 | 6 | 9 | 2 | 0 | 1 | 3 |
322,683 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_bayes.py | sklearn.linear_model._bayes.ARDRegression | from ._base import LinearModel, _preprocess_data, _rescale_data
from ..base import RegressorMixin, _fit_context
from scipy.linalg import pinvh
from ..utils.extmath import fast_logdet
from ..utils.validation import _check_sample_weight, validate_data
from math import log
from ..utils._param_validation import Interval
fr... |
class ARDRegression(RegressorMixin, LinearModel):
'''Bayesian ARD regression.
Fit the weights of a regression model, using an ARD prior. The weights of
the regression model are assumed to be in Gaussian distributions.
Also estimate the parameters lambda (precisions of the distributions of the
weigh... | 8 | 3 | 36 | 4 | 23 | 8 | 2 | 0.98 | 2 | 4 | 0 | 0 | 5 | 19 | 5 | 62 | 359 | 64 | 149 | 75 | 127 | 146 | 95 | 60 | 88 | 8 | 4 | 3 | 14 |
322,684 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_bayes.py | sklearn.linear_model._bayes.BayesianRidge | from ..utils._param_validation import Interval
import numpy as np
from ._base import LinearModel, _preprocess_data, _rescale_data
from numbers import Integral, Real
from ..utils.validation import _check_sample_weight, validate_data
from ..base import RegressorMixin, _fit_context
from scipy import linalg
from math impor... |
class BayesianRidge(RegressorMixin, LinearModel):
'''Bayesian ridge regression.
Fit a Bayesian ridge model. See the Notes section for details on this
implementation and the optimization of the regularization parameters
lambda (precision of the weights) and alpha (precision of the noise).
Read more ... | 7 | 5 | 54 | 7 | 35 | 13 | 3 | 0.92 | 2 | 4 | 0 | 0 | 5 | 20 | 5 | 62 | 436 | 74 | 189 | 84 | 163 | 173 | 105 | 64 | 99 | 10 | 4 | 3 | 17 |
322,685 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.ElasticNet | from ..utils.extmath import safe_sparse_dot
from sklearn.utils import metadata_routing
import numbers
from ._base import LinearModel, _pre_fit, _preprocess_data
import numpy as np
from numbers import Integral, Real
from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
import warnings
from ... |
class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
'''Linear regression with combined L1 and L2 priors as regularizer.
Minimizes the objective function::
1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_... | 8 | 4 | 50 | 5 | 29 | 16 | 4 | 1.23 | 3 | 4 | 0 | 1 | 5 | 14 | 5 | 63 | 433 | 65 | 165 | 51 | 143 | 203 | 77 | 35 | 71 | 15 | 4 | 2 | 20 |
322,686 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.ElasticNetCV | from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
from ..base import MultiOutputMixin, RegressorMixin, _fit_context
from numbers import Integral, Real
class ElasticNetCV(RegressorMixin, LinearModelCV):
"""Elastic Net model with iterative fitting along a regularization path.
S... |
class ElasticNetCV(RegressorMixin, LinearModelCV):
'''Elastic Net model with iterative fitting along a regularization path.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <elastic_net>`.
Parameters
----------
l1_ratio : float or list of float, defau... | 5 | 2 | 19 | 2 | 10 | 8 | 1 | 3.91 | 2 | 2 | 1 | 0 | 4 | 15 | 4 | 69 | 287 | 61 | 46 | 40 | 23 | 180 | 25 | 22 | 20 | 1 | 5 | 0 | 4 |
322,687 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.Lasso | class Lasso(ElasticNet):
"""Linear Model trained with L1 prior as regularizer (aka the Lasso).
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Technically the Lasso model is optimizing the same objective function as
the Elastic Net with ``l1_... | class Lasso(ElasticNet):
'''Linear Model trained with L1 prior as regularizer (aka the Lasso).
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Technically the Lasso model is optimizing the same objective function as
the Elastic Net with ``l1_rat... | 2 | 1 | 27 | 0 | 27 | 0 | 1 | 3.52 | 1 | 1 | 0 | 2 | 1 | 0 | 1 | 64 | 186 | 37 | 33 | 17 | 18 | 116 | 6 | 4 | 4 | 1 | 5 | 0 | 1 |
322,688 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.LassoCV | from ..base import MultiOutputMixin, RegressorMixin, _fit_context
class LassoCV(RegressorMixin, LinearModelCV):
"""Lasso linear model with iterative fitting along a regularization path.
See glossary entry for :term:`cross-validation estimator`.
The best model is selected by cross-validation.
The opt... |
class LassoCV(RegressorMixin, LinearModelCV):
'''Lasso linear model with iterative fitting along a regularization path.
See glossary entry for :term:`cross-validation estimator`.
The best model is selected by cross-validation.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||... | 5 | 2 | 19 | 2 | 10 | 8 | 1 | 3.95 | 2 | 2 | 1 | 0 | 4 | 0 | 4 | 69 | 263 | 55 | 42 | 23 | 20 | 166 | 10 | 6 | 5 | 1 | 5 | 0 | 4 |
322,689 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.LinearModelCV | from joblib import effective_n_jobs
from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
from ..model_selection import check_cv
from ..utils.parallel import Parallel, delayed
import warnings
import numpy as np
from ..utils.metadata_routing import _routing_enabled, process_routing
from ..u... |
class LinearModelCV(MultiOutputMixin, LinearModel, ABC):
'''Base class for iterative model fitting along a regularization path.'''
@abstractmethod
def __init__(self, eps=0.001, n_alphas='deprecated', alphas='warn', fit_intercept=True, precompute='auto', max_iter=1000, tol=0.0001, copy_X=True, cv=None, verb... | 14 | 6 | 55 | 5 | 37 | 13 | 5 | 0.31 | 3 | 17 | 4 | 4 | 6 | 23 | 7 | 63 | 427 | 45 | 292 | 83 | 262 | 90 | 137 | 62 | 129 | 29 | 4 | 3 | 35 |
322,690 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.MultiTaskElasticNet | from ._base import LinearModel, _pre_fit, _preprocess_data
import numpy as np
from ..base import MultiOutputMixin, RegressorMixin, _fit_context
from ..utils.validation import _check_sample_weight, check_consistent_length, check_is_fitted, check_random_state, column_or_1d, has_fit_parameter, validate_data
from . import ... |
class MultiTaskElasticNet(Lasso):
'''Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.
The optimization objective for MultiTaskElasticNet is::
(1 / (2 * n_samples)) * ||Y - XW||_Fro^2
+ alpha * l1_ratio * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
... | 5 | 2 | 39 | 5 | 26 | 8 | 2 | 1.37 | 1 | 3 | 0 | 1 | 3 | 13 | 3 | 67 | 251 | 48 | 86 | 42 | 69 | 118 | 44 | 27 | 40 | 4 | 6 | 1 | 6 |
322,691 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.MultiTaskElasticNetCV | from numbers import Integral, Real
from ..base import MultiOutputMixin, RegressorMixin, _fit_context
from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV):
"""Multi-task L1/L2 ElasticNet with built-in cross-validation.
Se... |
class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV):
'''Multi-task L1/L2 ElasticNet with built-in cross-validation.
See glossary entry for :term:`cross-validation estimator`.
The optimization objective for MultiTaskElasticNet is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2
+ alpha * l1... | 6 | 2 | 13 | 1 | 8 | 4 | 1 | 3.4 | 2 | 2 | 1 | 0 | 5 | 13 | 5 | 70 | 268 | 57 | 48 | 38 | 26 | 163 | 29 | 22 | 23 | 1 | 5 | 0 | 5 |
322,692 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.MultiTaskLasso | class MultiTaskLasso(MultiTaskElasticNet):
"""Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
Where::
||W||_21 = \\sum_i \\sqrt{\\sum_j w_{ij}^2}
i.e. the sum o... | class MultiTaskLasso(MultiTaskElasticNet):
'''Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm o... | 2 | 1 | 21 | 0 | 21 | 0 | 1 | 3.27 | 1 | 0 | 0 | 0 | 1 | 9 | 1 | 68 | 141 | 30 | 26 | 23 | 13 | 85 | 13 | 12 | 11 | 1 | 7 | 0 | 1 |
322,693 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_coordinate_descent.py | sklearn.linear_model._coordinate_descent.MultiTaskLassoCV | from ..base import MultiOutputMixin, RegressorMixin, _fit_context
class MultiTaskLassoCV(RegressorMixin, LinearModelCV):
"""Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
See glossary entry for :term:`cross-validation estimator`.
The optimization objective for MultiTaskLasso is::
... |
class MultiTaskLassoCV(RegressorMixin, LinearModelCV):
'''Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
See glossary entry for :term:`cross-validation estimator`.
The optimization objective for MultiTaskLasso is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * ||W||_21
... | 6 | 2 | 13 | 1 | 8 | 4 | 1 | 3.17 | 2 | 2 | 1 | 0 | 5 | 0 | 5 | 70 | 251 | 55 | 47 | 24 | 26 | 149 | 17 | 9 | 11 | 1 | 5 | 0 | 5 |
322,694 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_glm/_newton_solver.py | sklearn.linear_model._glm._newton_solver.NewtonCholeskySolver | from ...exceptions import ConvergenceWarning
import scipy.optimize
import scipy.linalg
import warnings
import numpy as np
class NewtonCholeskySolver(NewtonSolver):
"""Cholesky based Newton solver.
Inner solver for finding the Newton step H w_newton = -g uses Cholesky based linear
solver.
"""
def ... |
class NewtonCholeskySolver(NewtonSolver):
'''Cholesky based Newton solver.
Inner solver for finding the Newton step H w_newton = -g uses Cholesky based linear
solver.
'''
def setup(self, X, y, sample_weight):
pass
def update_gradient_hessian(self, X, y, sample_weight):
pass
... | 5 | 1 | 41 | 1 | 28 | 14 | 4 | 0.53 | 1 | 4 | 1 | 0 | 4 | 11 | 4 | 33 | 173 | 7 | 111 | 24 | 106 | 59 | 62 | 21 | 57 | 11 | 5 | 4 | 17 |
322,695 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_glm/_newton_solver.py | sklearn.linear_model._glm._newton_solver.NewtonSolver | import numpy as np
from abc import ABC, abstractmethod
from .._linear_loss import LinearModelLoss
from ...exceptions import ConvergenceWarning
from ..._loss.loss import HalfSquaredError
from ...utils.optimize import _check_optimize_result
from ...utils.fixes import _get_additional_lbfgs_options_dict
import scipy.optimi... |
class NewtonSolver(ABC):
'''Newton solver for GLMs.
This class implements Newton/2nd-order optimization routines for GLMs. Each Newton
iteration aims at finding the Newton step which is done by the inner solver. With
Hessian H, gradient g and coefficients coef, one step solves:
H @ coef_newton ... | 12 | 9 | 35 | 4 | 21 | 11 | 4 | 0.9 | 1 | 5 | 3 | 1 | 9 | 16 | 9 | 29 | 422 | 67 | 190 | 57 | 168 | 171 | 113 | 45 | 103 | 12 | 4 | 3 | 32 |
322,696 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_glm/glm.py | sklearn.linear_model._glm.glm.GammaRegressor | from ..._loss.loss import HalfGammaLoss, HalfPoissonLoss, HalfSquaredError, HalfTweedieLoss, HalfTweedieLossIdentity
class GammaRegressor(_GeneralizedLinearRegressor):
"""Generalized Linear Model with a Gamma distribution.
This regressor uses the 'log' link function.
Read more in the :ref:`User Guide <Ge... |
class GammaRegressor(_GeneralizedLinearRegressor):
'''Generalized Linear Model with a Gamma distribution.
This regressor uses the 'log' link function.
Read more in the :ref:`User Guide <Generalized_linear_models>`.
.. versionadded:: 0.23
Parameters
----------
alpha : float, default=1
... | 3 | 1 | 11 | 0 | 11 | 0 | 1 | 3.04 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 42 | 130 | 25 | 26 | 14 | 13 | 79 | 6 | 4 | 3 | 1 | 3 | 0 | 2 |
322,697 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_glm/glm.py | sklearn.linear_model._glm.glm.PoissonRegressor | from ..._loss.loss import HalfGammaLoss, HalfPoissonLoss, HalfSquaredError, HalfTweedieLoss, HalfTweedieLossIdentity
class PoissonRegressor(_GeneralizedLinearRegressor):
"""Generalized Linear Model with a Poisson distribution.
This regressor uses the 'log' link function.
Read more in the :ref:`User Guide... |
class PoissonRegressor(_GeneralizedLinearRegressor):
'''Generalized Linear Model with a Poisson distribution.
This regressor uses the 'log' link function.
Read more in the :ref:`User Guide <Generalized_linear_models>`.
.. versionadded:: 0.23
Parameters
----------
alpha : float, default=1
... | 3 | 1 | 11 | 0 | 11 | 0 | 1 | 3 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 42 | 129 | 25 | 26 | 14 | 13 | 78 | 6 | 4 | 3 | 1 | 3 | 0 | 2 |
322,698 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_glm/glm.py | sklearn.linear_model._glm.glm.TweedieRegressor | from ...utils._param_validation import Hidden, Interval, StrOptions
from numbers import Integral, Real
from ..._loss.loss import HalfGammaLoss, HalfPoissonLoss, HalfSquaredError, HalfTweedieLoss, HalfTweedieLossIdentity
class TweedieRegressor(_GeneralizedLinearRegressor):
"""Generalized Linear Model with a Tweedie... |
class TweedieRegressor(_GeneralizedLinearRegressor):
'''Generalized Linear Model with a Tweedie distribution.
This estimator can be used to model different GLMs depending on the
``power`` parameter, which determines the underlying distribution.
Read more in the :ref:`User Guide <Generalized_linear_mode... | 3 | 1 | 19 | 1 | 17 | 1 | 3 | 2.65 | 1 | 3 | 2 | 0 | 2 | 2 | 2 | 42 | 178 | 32 | 40 | 18 | 25 | 106 | 15 | 6 | 12 | 5 | 3 | 2 | 6 |
322,699 | etsi-ai/etsi-watchdog | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/sklearn/linear_model/_glm/glm.py | sklearn.linear_model._glm.glm._GeneralizedLinearRegressor | from ...utils._param_validation import Hidden, Interval, StrOptions
from ...utils.optimize import _check_optimize_result
from ..._loss.loss import HalfGammaLoss, HalfPoissonLoss, HalfSquaredError, HalfTweedieLoss, HalfTweedieLossIdentity
from ...utils import check_array
import numpy as np
from ...utils.fixes import _ge... |
class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):
'''Regression via a penalized Generalized Linear Model (GLM).
GLMs based on a reproductive Exponential Dispersion Model (EDM) aim at fitting and
predicting the mean of the target y as y_pred=h(X*w) with coefficients w.
Therefore, the fit... | 9 | 6 | 44 | 5 | 24 | 16 | 3 | 1.07 | 2 | 10 | 4 | 4 | 7 | 11 | 7 | 40 | 438 | 64 | 182 | 50 | 163 | 195 | 83 | 39 | 75 | 11 | 2 | 2 | 20 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.