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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
322,400 | 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/scipy/stats/_stats_py.py | scipy.stats._stats_py._SimpleStudentT | import scipy.special as special
class _SimpleStudentT:
def __init__(self, df):
self.df = df
def cdf(self, t):
return special.stdtr(self.df, t)
def sf(self, t):
return special.stdtr(self.df, -t) |
class _SimpleStudentT:
def __init__(self, df):
pass
def cdf(self, t):
pass
def sf(self, t):
pass | 4 | 0 | 2 | 0 | 2 | 0 | 1 | 0.43 | 0 | 0 | 0 | 0 | 3 | 1 | 3 | 3 | 12 | 2 | 7 | 5 | 3 | 3 | 7 | 5 | 3 | 1 | 0 | 0 | 3 |
322,401 | 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/scipy/stats/_survival.py | scipy.stats._survival.ECDFResult | from dataclasses import dataclass, field
@dataclass
class ECDFResult:
""" Result object returned by `scipy.stats.ecdf`
Attributes
----------
cdf : `~scipy.stats._result_classes.EmpiricalDistributionFunction`
An object representing the empirical cumulative distribution function.
sf : `~scip... | @dataclass
class ECDFResult:
''' Result object returned by `scipy.stats.ecdf`
Attributes
----------
cdf : `~scipy.stats._result_classes.EmpiricalDistributionFunction`
An object representing the empirical cumulative distribution function.
sf : `~scipy.stats._result_classes.EmpiricalDistributi... | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 1.5 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 17 | 2 | 6 | 2 | 4 | 9 | 6 | 2 | 4 | 1 | 0 | 0 | 1 |
322,402 | 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/scipy/stats/_survival.py | scipy.stats._survival.EmpiricalDistributionFunction | from dataclasses import dataclass, field
import warnings
from scipy import special, interpolate, stats
import numpy as np
from scipy.stats._common import ConfidenceInterval
@dataclass
class EmpiricalDistributionFunction:
"""An empirical distribution function produced by `scipy.stats.ecdf`
Attributes
-----... | @dataclass
class EmpiricalDistributionFunction:
'''An empirical distribution function produced by `scipy.stats.ecdf`
Attributes
----------
quantiles : ndarray
The unique values of the sample from which the
`EmpiricalDistributionFunction` was estimated.
probabilities : ndarray
... | 8 | 4 | 29 | 5 | 14 | 11 | 3 | 0.91 | 0 | 6 | 0 | 0 | 6 | 1 | 6 | 6 | 199 | 38 | 88 | 42 | 79 | 80 | 81 | 41 | 72 | 5 | 0 | 1 | 15 |
322,403 | 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/scipy/stats/_survival.py | scipy.stats._survival.LogRankResult | from dataclasses import dataclass, field
import numpy as np
@dataclass
class LogRankResult:
"""Result object returned by `scipy.stats.logrank`.
Attributes
----------
statistic : float ndarray
The computed statistic (defined below). Its magnitude is the
square root of the magnitude retu... | @dataclass
class LogRankResult:
'''Result object returned by `scipy.stats.logrank`.
Attributes
----------
statistic : float ndarray
The computed statistic (defined below). Its magnitude is the
square root of the magnitude returned by most other logrank test
implementations.
p... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 3.33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 1 | 3 | 1 | 2 | 10 | 3 | 1 | 2 | 0 | 0 | 0 | 0 |
322,404 | 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/scipy/stats/_warnings_errors.py | scipy.stats._warnings_errors.ConstantInputWarning | class ConstantInputWarning(DegenerateDataWarning):
"""Warns when all values in data are exactly equal."""
def __init__(self, msg=None):
if msg is None:
msg = 'All values in data are exactly equal; results may not be reliable.'
self.args = (msg,) | class ConstantInputWarning(DegenerateDataWarning):
'''Warns when all values in data are exactly equal.'''
def __init__(self, msg=None):
pass | 2 | 1 | 5 | 0 | 5 | 0 | 2 | 0.17 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 14 | 7 | 0 | 6 | 3 | 4 | 1 | 5 | 3 | 3 | 2 | 6 | 1 | 2 |
322,405 | 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/scipy/stats/_warnings_errors.py | scipy.stats._warnings_errors.DegenerateDataWarning | class DegenerateDataWarning(RuntimeWarning):
"""Warns when data is degenerate and results may not be reliable."""
def __init__(self, msg=None):
if msg is None:
msg = 'Degenerate data encountered; results may not be reliable.'
self.args = (msg,) | class DegenerateDataWarning(RuntimeWarning):
'''Warns when data is degenerate and results may not be reliable.'''
def __init__(self, msg=None):
pass | 2 | 1 | 4 | 0 | 4 | 0 | 2 | 0.2 | 1 | 0 | 0 | 2 | 1 | 1 | 1 | 13 | 6 | 0 | 5 | 3 | 3 | 1 | 5 | 3 | 3 | 2 | 5 | 1 | 2 |
322,406 | 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/scipy/stats/_warnings_errors.py | scipy.stats._warnings_errors.FitError | class FitError(RuntimeError):
"""Represents an error condition when fitting a distribution to data."""
def __init__(self, msg=None):
if msg is None:
msg = 'An error occurred when fitting a distribution to data.'
self.args = (msg,) | class FitError(RuntimeError):
'''Represents an error condition when fitting a distribution to data.'''
def __init__(self, msg=None):
pass | 2 | 1 | 4 | 0 | 4 | 0 | 2 | 0.2 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 12 | 6 | 0 | 5 | 3 | 3 | 1 | 5 | 3 | 3 | 2 | 4 | 1 | 2 |
322,407 | 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/scipy/stats/_warnings_errors.py | scipy.stats._warnings_errors.NearConstantInputWarning | class NearConstantInputWarning(DegenerateDataWarning):
"""Warns when all values in data are nearly equal."""
def __init__(self, msg=None):
if msg is None:
msg = 'All values in data are nearly equal; results may not be reliable.'
self.args = (msg,) | class NearConstantInputWarning(DegenerateDataWarning):
'''Warns when all values in data are nearly equal.'''
def __init__(self, msg=None):
pass | 2 | 1 | 5 | 0 | 5 | 0 | 2 | 0.17 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 14 | 7 | 0 | 6 | 3 | 4 | 1 | 5 | 3 | 3 | 2 | 6 | 1 | 2 |
322,408 | 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/scipy/stats/_wilcoxon.py | scipy.stats._wilcoxon.WilcoxonDistribution | from ._hypotests import _get_wilcoxon_distr
import numpy as np
import scipy._lib.array_api_extra as xpx
class WilcoxonDistribution:
def __init__(self, n):
n = np.asarray(n).astype(int, copy=False)
self.n = n
self._dists = {ni: _get_wilcoxon_distr(ni) for ni in np.unique(n)}
def _cdf1(... |
class WilcoxonDistribution:
def __init__(self, n):
pass
def _cdf1(self, k, n):
pass
def _cdf1(self, k, n):
pass
def _sf1(self, k, n):
pass
def _sf1(self, k, n):
pass
def mean(self):
pass
def _prep(self, k):
pass
def cdf(sel... | 10 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 9 | 2 | 9 | 9 | 43 | 9 | 34 | 18 | 24 | 0 | 28 | 18 | 18 | 1 | 0 | 0 | 9 |
322,409 | 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/six.py | six.Module_six_moves_urllib | import types
class Module_six_moves_urllib(types.ModuleType):
"""Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
__path__ = []
parse = _importer._get_module('moves.urllib_parse')
error = _importer._get_module('moves.urllib_error')
request = _importer._get_module('moves.... |
class Module_six_moves_urllib(types.ModuleType):
'''Create a six.moves.urllib namespace that resembles the Python 3 namespace'''
def __dir__(self):
pass | 2 | 1 | 2 | 0 | 2 | 0 | 1 | 0.22 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 12 | 2 | 9 | 8 | 7 | 2 | 9 | 8 | 7 | 1 | 1 | 0 | 1 |
322,410 | 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/six.py | six.Module_six_moves_urllib_error | class Module_six_moves_urllib_error(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_error""" | class Module_six_moves_urllib_error(_LazyModule):
'''Lazy loading of moved objects in six.moves.urllib_error'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
322,411 | 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/six.py | six.Module_six_moves_urllib_parse | class Module_six_moves_urllib_parse(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_parse""" | class Module_six_moves_urllib_parse(_LazyModule):
'''Lazy loading of moved objects in six.moves.urllib_parse'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
322,412 | 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/six.py | six.Module_six_moves_urllib_request | class Module_six_moves_urllib_request(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_request""" | class Module_six_moves_urllib_request(_LazyModule):
'''Lazy loading of moved objects in six.moves.urllib_request'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
322,413 | 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/six.py | six.Module_six_moves_urllib_response | class Module_six_moves_urllib_response(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_response""" | class Module_six_moves_urllib_response(_LazyModule):
'''Lazy loading of moved objects in six.moves.urllib_response'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
322,414 | 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/six.py | six.Module_six_moves_urllib_robotparser | class Module_six_moves_urllib_robotparser(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_robotparser""" | class Module_six_moves_urllib_robotparser(_LazyModule):
'''Lazy loading of moved objects in six.moves.urllib_robotparser'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
322,415 | 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/six.py | six.MovedAttribute | class MovedAttribute(_LazyDescr):
def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
super(MovedAttribute, self).__init__(name)
if PY3:
if new_mod is None:
new_mod = name
self.mod = new_mod
if new_attr is None:
... | class MovedAttribute(_LazyDescr):
def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
pass
def _resolve(self):
pass | 3 | 0 | 10 | 0 | 10 | 0 | 4 | 0 | 1 | 1 | 0 | 0 | 2 | 2 | 2 | 4 | 23 | 2 | 21 | 6 | 18 | 0 | 19 | 6 | 16 | 6 | 2 | 3 | 7 |
322,416 | 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/six.py | six.MovedModule | class MovedModule(_LazyDescr):
def __init__(self, name, old, new=None):
super(MovedModule, self).__init__(name)
if PY3:
if new is None:
new = name
self.mod = new
else:
self.mod = old
def _resolve(self):
return _import_module(s... | class MovedModule(_LazyDescr):
def __init__(self, name, old, new=None):
pass
def _resolve(self):
pass
def __getattr__(self, attr):
pass | 4 | 0 | 5 | 0 | 5 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 3 | 1 | 3 | 5 | 19 | 3 | 16 | 7 | 12 | 0 | 15 | 7 | 11 | 3 | 2 | 2 | 5 |
322,417 | 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/six.py | six._LazyDescr | class _LazyDescr(object):
def __init__(self, name):
self.name = name
def __get__(self, obj, tp):
result = self._resolve()
setattr(obj, self.name, result)
try:
delattr(obj.__class__, self.name)
except AttributeError:
pass
return result | class _LazyDescr(object):
def __init__(self, name):
pass
def __get__(self, obj, tp):
pass | 3 | 0 | 6 | 0 | 5 | 2 | 2 | 0.27 | 1 | 1 | 0 | 2 | 2 | 1 | 2 | 2 | 15 | 2 | 11 | 5 | 8 | 3 | 11 | 5 | 8 | 2 | 1 | 1 | 3 |
322,418 | 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/six.py | six._LazyModule | import types
class _LazyModule(types.ModuleType):
def __init__(self, name):
super(_LazyModule, self).__init__(name)
self.__doc__ = self.__class__.__doc__
def __dir__(self):
attrs = ['__doc__', '__name__']
attrs += [attr.name for attr in self._moved_attributes]
return a... |
class _LazyModule(types.ModuleType):
def __init__(self, name):
pass
def __dir__(self):
pass | 3 | 0 | 4 | 0 | 4 | 0 | 1 | 0.11 | 1 | 1 | 0 | 6 | 2 | 1 | 2 | 2 | 13 | 3 | 9 | 6 | 6 | 1 | 9 | 6 | 6 | 1 | 1 | 0 | 2 |
322,419 | 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/six.py | six._MovedItems | class _MovedItems(_LazyModule):
"""Lazy loading of moved objects"""
__path__ = [] | class _MovedItems(_LazyModule):
'''Lazy loading of moved objects'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 1 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 0 | 2 | 0 | 0 |
322,420 | 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/six.py | six._SixMetaPathImporter | import sys
class _SixMetaPathImporter(object):
"""
A meta path importer to import six.moves and its submodules.
This class implements a PEP302 finder and loader. It should be compatible
with Python 2.5 and all existing versions of Python3
"""
def __init__(self, six_module_name):
self.... |
class _SixMetaPathImporter(object):
'''
A meta path importer to import six.moves and its submodules.
This class implements a PEP302 finder and loader. It should be compatible
with Python 2.5 and all existing versions of Python3
'''
def __init__(self, six_module_name):
pass
def _ad... | 12 | 3 | 5 | 0 | 4 | 1 | 2 | 0.34 | 1 | 3 | 1 | 0 | 11 | 2 | 11 | 11 | 72 | 15 | 44 | 17 | 32 | 15 | 43 | 17 | 31 | 3 | 1 | 1 | 17 |
322,421 | 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/_loss/link.py | sklearn._loss.link.BaseLink | import numpy as np
from abc import ABC, abstractmethod
class BaseLink(ABC):
"""Abstract base class for differentiable, invertible link functions.
Convention:
- link function g: raw_prediction = g(y_pred)
- inverse link h: y_pred = h(raw_prediction)
For (generalized) linear models, `raw_pr... |
class BaseLink(ABC):
'''Abstract base class for differentiable, invertible link functions.
Convention:
- link function g: raw_prediction = g(y_pred)
- inverse link h: y_pred = h(raw_prediction)
For (generalized) linear models, `raw_prediction = X @ coef` is the so
called linear predicto... | 5 | 3 | 20 | 3 | 1 | 16 | 1 | 6.57 | 1 | 0 | 0 | 5 | 2 | 0 | 2 | 22 | 65 | 13 | 7 | 7 | 2 | 46 | 5 | 5 | 2 | 1 | 4 | 0 | 2 |
322,422 | 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/_loss/link.py | sklearn._loss.link.HalfLogitLink | from scipy.special import expit, logit
class HalfLogitLink(BaseLink):
"""Half the logit link function g(x)=1/2 * logit(x).
Used for the exponential loss.
"""
interval_y_pred = Interval(0, 1, False, False)
def link(self, y_pred, out=None):
out = logit(y_pred, out=out)
out *= 0.5
... |
class HalfLogitLink(BaseLink):
'''Half the logit link function g(x)=1/2 * logit(x).
Used for the exponential loss.
'''
def link(self, y_pred, out=None):
pass
def inverse(self, raw_prediction, out=None):
pass | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 0.38 | 1 | 0 | 0 | 0 | 2 | 0 | 2 | 24 | 15 | 4 | 8 | 4 | 5 | 3 | 8 | 4 | 5 | 1 | 5 | 0 | 2 |
322,423 | 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/_loss/link.py | sklearn._loss.link.IdentityLink | import numpy as np
class IdentityLink(BaseLink):
"""The identity link function g(x)=x."""
def link(self, y_pred, out=None):
if out is not None:
np.copyto(out, y_pred)
return out
else:
return y_pred
inverse = link |
class IdentityLink(BaseLink):
'''The identity link function g(x)=x.'''
def link(self, y_pred, out=None):
pass | 2 | 1 | 6 | 0 | 6 | 0 | 2 | 0.13 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 23 | 11 | 2 | 8 | 3 | 6 | 1 | 7 | 3 | 5 | 2 | 5 | 1 | 2 |
322,424 | 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/_loss/link.py | sklearn._loss.link.Interval | from dataclasses import dataclass
import numpy as np
@dataclass
class Interval:
low: float
high: float
low_inclusive: bool
high_inclusive: bool
def __post_init__(self):
"""Check that low <= high"""
if self.low > self.high:
raise ValueError(f'One must have low <= high; g... | @dataclass
class Interval:
def __post_init__(self):
'''Check that low <= high'''
pass
def includes(self, x):
'''Test whether all values of x are in interval range.
Parameters
----------
x : ndarray
Array whose elements are tested to be in interval ra... | 4 | 2 | 17 | 3 | 9 | 6 | 3 | 0.5 | 0 | 2 | 0 | 0 | 2 | 0 | 2 | 2 | 40 | 7 | 22 | 5 | 19 | 11 | 18 | 5 | 15 | 4 | 0 | 1 | 6 |
322,425 | 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/_loss/link.py | sklearn._loss.link.LogLink | import numpy as np
class LogLink(BaseLink):
"""The log link function g(x)=log(x)."""
interval_y_pred = Interval(0, np.inf, False, False)
def link(self, y_pred, out=None):
return np.log(y_pred, out=out)
def inverse(self, raw_prediction, out=None):
return np.exp(raw_prediction, out=out) |
class LogLink(BaseLink):
'''The log link function g(x)=log(x).'''
def link(self, y_pred, out=None):
pass
def inverse(self, raw_prediction, out=None):
pass | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 0.17 | 1 | 0 | 0 | 0 | 2 | 0 | 2 | 24 | 10 | 3 | 6 | 4 | 3 | 1 | 6 | 4 | 3 | 1 | 5 | 0 | 2 |
322,426 | 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/_loss/link.py | sklearn._loss.link.LogitLink | from scipy.special import expit, logit
class LogitLink(BaseLink):
"""The logit link function g(x)=logit(x)."""
interval_y_pred = Interval(0, 1, False, False)
def link(self, y_pred, out=None):
return logit(y_pred, out=out)
def inverse(self, raw_prediction, out=None):
return expit(raw_p... |
class LogitLink(BaseLink):
'''The logit link function g(x)=logit(x).'''
def link(self, y_pred, out=None):
pass
def inverse(self, raw_prediction, out=None):
pass | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 0.17 | 1 | 0 | 0 | 0 | 2 | 0 | 2 | 24 | 10 | 3 | 6 | 4 | 3 | 1 | 6 | 4 | 3 | 1 | 5 | 0 | 2 |
322,427 | 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/_loss/link.py | sklearn._loss.link.MultinomialLogit | from ..utils.extmath import softmax
from scipy.stats import gmean
import numpy as np
class MultinomialLogit(BaseLink):
"""The symmetric multinomial logit function.
Convention:
- y_pred.shape = raw_prediction.shape = (n_samples, n_classes)
Notes:
- The inverse link h is the softmax functio... |
class MultinomialLogit(BaseLink):
'''The symmetric multinomial logit function.
Convention:
- y_pred.shape = raw_prediction.shape = (n_samples, n_classes)
Notes:
- The inverse link h is the softmax function.
- The sum is over the second axis, i.e. axis=1 (n_classes).
We have to c... | 4 | 1 | 4 | 0 | 4 | 0 | 1 | 2.2 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 25 | 64 | 16 | 15 | 7 | 11 | 33 | 14 | 7 | 10 | 2 | 5 | 1 | 4 |
322,428 | 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/_loss/loss.py | sklearn._loss.loss.AbsoluteError | import numpy as np
from ..utils.stats import _weighted_percentile
from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from .link import HalfLogit... |
class AbsoluteError(BaseLoss):
'''Absolute error with identity link, for regression.
Domain:
y_true and y_pred all real numbers
Link:
y_pred = raw_prediction
For a given sample x_i, the absolute error is defined as::
loss(x_i) = |y_true_i - raw_prediction_i|
Note that the exact hess... | 3 | 2 | 7 | 1 | 5 | 2 | 2 | 1.25 | 1 | 2 | 1 | 0 | 2 | 2 | 2 | 13 | 36 | 9 | 12 | 7 | 9 | 15 | 11 | 7 | 8 | 2 | 1 | 1 | 3 |
322,429 | 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/_loss/loss.py | sklearn._loss.loss.BaseLoss | import numpy as np
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit
class BaseLoss:
"""Base class for a loss function of 1-dimensional targets.
Conventions:
- y_true.shape = sample_weight.shape = (n_samples,)
- y_pred.shape = raw_prediction.shape =... |
class BaseLoss:
'''Base class for a loss function of 1-dimensional targets.
Conventions:
- y_true.shape = sample_weight.shape = (n_samples,)
- y_pred.shape = raw_prediction.shape = (n_samples,)
- If is_multiclass is true (multiclass classification), then
y_pred.shape = raw_pre... | 12 | 11 | 34 | 3 | 15 | 16 | 3 | 1.32 | 0 | 3 | 1 | 11 | 11 | 7 | 11 | 11 | 443 | 53 | 168 | 59 | 126 | 222 | 89 | 29 | 77 | 7 | 0 | 2 | 35 |
322,430 | 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/_loss/loss.py | sklearn._loss.loss.ExponentialLoss | from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit... |
class ExponentialLoss(BaseLoss):
'''Exponential loss with (half) logit link, for binary classification.
This is also know as boosting loss.
Domain:
y_true in [0, 1], i.e. regression on the unit interval
y_pred in (0, 1), i.e. boundaries excluded
Link:
y_pred = expit(2 * raw_prediction)
... | 4 | 2 | 11 | 1 | 6 | 4 | 2 | 1.85 | 1 | 3 | 2 | 0 | 3 | 1 | 3 | 14 | 71 | 14 | 20 | 7 | 16 | 37 | 16 | 7 | 12 | 2 | 1 | 1 | 5 |
322,431 | 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/_loss/loss.py | sklearn._loss.loss.HalfBinomialLoss | from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit
from scipy.special import xlogy
import numpy as np
from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, Cy... |
class HalfBinomialLoss(BaseLoss):
'''Half Binomial deviance loss with logit link, for binary classification.
This is also know as binary cross entropy, log-loss and logistic loss.
Domain:
y_true in [0, 1], i.e. regression on the unit interval
y_pred in (0, 1), i.e. boundaries excluded
Link:
... | 4 | 2 | 11 | 1 | 6 | 4 | 2 | 1.65 | 1 | 3 | 2 | 0 | 3 | 1 | 3 | 14 | 67 | 14 | 20 | 7 | 16 | 33 | 16 | 7 | 12 | 2 | 1 | 1 | 5 |
322,432 | 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/_loss/loss.py | sklearn._loss.loss.HalfGammaLoss | import numpy as np
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit
from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuber... |
class HalfGammaLoss(BaseLoss):
'''Half Gamma deviance loss with log-link, for regression.
Domain:
y_true and y_pred in positive real numbers
Link:
y_pred = exp(raw_prediction)
For a given sample x_i, half Gamma deviance loss is defined as::
loss(x_i) = log(exp(raw_prediction_i)/y_true_i... | 3 | 1 | 4 | 0 | 4 | 0 | 2 | 1.44 | 1 | 3 | 2 | 0 | 2 | 1 | 2 | 13 | 29 | 7 | 9 | 5 | 6 | 13 | 9 | 5 | 6 | 2 | 1 | 1 | 3 |
322,433 | 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/_loss/loss.py | sklearn._loss.loss.HalfMultinomialLoss | from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
import numpy as np
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLin... |
class HalfMultinomialLoss(BaseLoss):
'''Categorical cross-entropy loss, for multiclass classification.
Domain:
y_true in {0, 1, 2, 3, .., n_classes - 1}
y_pred has n_classes elements, each element in (0, 1)
Link:
y_pred = softmax(raw_prediction)
Note: We assume y_true to be already label en... | 6 | 5 | 19 | 2 | 9 | 9 | 2 | 1.45 | 1 | 6 | 2 | 0 | 5 | 3 | 5 | 16 | 137 | 22 | 47 | 20 | 33 | 68 | 26 | 12 | 20 | 4 | 1 | 2 | 9 |
322,434 | 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/_loss/loss.py | sklearn._loss.loss.HalfPoissonLoss | import numpy as np
from scipy.special import xlogy
from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from .link import HalfLogitLink, IdentityL... |
class HalfPoissonLoss(BaseLoss):
'''Half Poisson deviance loss with log-link, for regression.
Domain:
y_true in non-negative real numbers
y_pred in positive real numbers
Link:
y_pred = exp(raw_prediction)
For a given sample x_i, half the Poisson deviance is defined as::
loss(x_i) = ... | 3 | 1 | 4 | 0 | 4 | 0 | 2 | 1.56 | 1 | 3 | 2 | 0 | 2 | 1 | 2 | 13 | 30 | 7 | 9 | 5 | 6 | 14 | 9 | 5 | 6 | 2 | 1 | 1 | 3 |
322,435 | 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/_loss/loss.py | sklearn._loss.loss.HalfSquaredError | from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit... |
class HalfSquaredError(BaseLoss):
'''Half squared error with identity link, for regression.
Domain:
y_true and y_pred all real numbers
Link:
y_pred = raw_prediction
For a given sample x_i, half squared error is defined as::
loss(x_i) = 0.5 * (y_true_i - raw_prediction_i)**2
The fact... | 2 | 1 | 3 | 0 | 3 | 0 | 1 | 2.75 | 1 | 2 | 1 | 0 | 1 | 1 | 1 | 12 | 21 | 6 | 4 | 3 | 2 | 11 | 4 | 3 | 2 | 1 | 1 | 0 | 1 |
322,436 | 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/_loss/loss.py | sklearn._loss.loss.HalfTweedieLoss | from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
import numpy as np
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLin... |
class HalfTweedieLoss(BaseLoss):
'''Half Tweedie deviance loss with log-link, for regression.
Domain:
y_true in real numbers for power <= 0
y_true in non-negative real numbers for 0 < power < 2
y_true in positive real numbers for 2 <= power
y_pred in positive real numbers
power in real numb... | 3 | 1 | 15 | 0 | 15 | 0 | 4 | 0.71 | 1 | 7 | 5 | 0 | 2 | 1 | 2 | 13 | 62 | 9 | 31 | 6 | 28 | 22 | 17 | 6 | 14 | 5 | 1 | 2 | 8 |
322,437 | 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/_loss/loss.py | sklearn._loss.loss.HalfTweedieLossIdentity | from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from .link import HalfLogitLink, IdentityLink, Interval, LogitLink, LogLink, MultinomialLogit... |
class HalfTweedieLossIdentity(BaseLoss):
'''Half Tweedie deviance loss with identity link, for regression.
Domain:
y_true in real numbers for power <= 0
y_true in non-negative real numbers for 0 < power < 2
y_true in positive real numbers for 2 <= power
y_pred in positive real numbers for power... | 2 | 1 | 16 | 1 | 15 | 0 | 4 | 1.25 | 1 | 4 | 2 | 0 | 1 | 2 | 1 | 12 | 44 | 8 | 16 | 4 | 14 | 20 | 10 | 4 | 8 | 4 | 1 | 1 | 4 |
322,438 | 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/_loss/loss.py | sklearn._loss.loss.HuberLoss | import numbers
from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from ..utils.stats import _weighted_percentile
from ..utils import check_scala... |
class HuberLoss(BaseLoss):
'''Huber loss, for regression.
Domain:
y_true and y_pred all real numbers
quantile in (0, 1)
Link:
y_pred = raw_prediction
For a given sample x_i, the Huber loss is defined as::
loss(x_i) = 1/2 * abserr**2 if abserr <= delta
... | 3 | 2 | 17 | 1 | 12 | 5 | 2 | 1.22 | 1 | 4 | 1 | 0 | 2 | 3 | 2 | 13 | 71 | 12 | 27 | 11 | 24 | 33 | 16 | 11 | 13 | 2 | 1 | 1 | 3 |
322,439 | 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/_loss/loss.py | sklearn._loss.loss.PinballLoss | from ..utils import check_scalar
from ._loss import CyAbsoluteError, CyExponentialLoss, CyHalfBinomialLoss, CyHalfGammaLoss, CyHalfMultinomialLoss, CyHalfPoissonLoss, CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, CyHuberLoss, CyPinballLoss
from .link import HalfLogitLink, IdentityLink, Interval, Log... |
class PinballLoss(BaseLoss):
'''Quantile loss aka pinball loss, for regression.
Domain:
y_true and y_pred all real numbers
quantile in (0, 1)
Link:
y_pred = raw_prediction
For a given sample x_i, the pinball loss is defined as::
loss(x_i) = rho_{quantile}(y_true_i - raw_prediction_i... | 3 | 2 | 14 | 1 | 11 | 2 | 2 | 0.96 | 1 | 4 | 1 | 0 | 2 | 2 | 2 | 13 | 61 | 12 | 25 | 7 | 22 | 24 | 12 | 7 | 9 | 2 | 1 | 1 | 3 |
322,440 | 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/base.py | sklearn.base.BaseEstimator | import warnings
from .utils._tags import ClassifierTags, RegressorTags, Tags, TargetTags, TransformerTags, get_tags
from .utils._repr_html.estimator import estimator_html_repr
from .utils._metadata_requests import _MetadataRequester, _routing_enabled
from .utils._repr_html.base import ReprHTMLMixin, _HTMLDocumentationL... |
class BaseEstimator(ReprHTMLMixin, _HTMLDocumentationLinkMixin, _MetadataRequester):
'''Base class for all estimators in scikit-learn.
Inheriting from this class provides default implementations of:
- setting and getting parameters used by `GridSearchCV` and friends;
- textual and HTML representation d... | 13 | 7 | 26 | 3 | 15 | 8 | 3 | 0.8 | 3 | 15 | 5 | 281 | 9 | 1 | 10 | 31 | 320 | 46 | 154 | 47 | 140 | 123 | 103 | 44 | 90 | 6 | 1 | 2 | 35 |
322,441 | 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/base.py | sklearn.base.BiclusterMixin | import numpy as np
from .utils.validation import _check_feature_names_in, _generate_get_feature_names_out, _is_fitted, check_array, check_is_fitted
class BiclusterMixin:
"""Mixin class for all bicluster estimators in scikit-learn.
This mixin defines the following functionality:
- `biclusters_` property t... |
class BiclusterMixin:
'''Mixin class for all bicluster estimators in scikit-learn.
This mixin defines the following functionality:
- `biclusters_` property that returns the row and column indicators;
- `get_indices` method that returns the row and column indices of a bicluster;
- `get_shape` method... | 6 | 5 | 17 | 3 | 3 | 11 | 1 | 4.47 | 0 | 1 | 0 | 2 | 4 | 0 | 4 | 4 | 100 | 18 | 15 | 10 | 9 | 67 | 14 | 9 | 9 | 1 | 0 | 0 | 4 |
322,442 | 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/base.py | sklearn.base.ClassNamePrefixFeaturesOutMixin | from .utils.validation import _check_feature_names_in, _generate_get_feature_names_out, _is_fitted, check_array, check_is_fitted
class ClassNamePrefixFeaturesOutMixin:
"""Mixin class for transformers that generate their own names by prefixing.
This mixin is useful when the transformer needs to generate its ow... |
class ClassNamePrefixFeaturesOutMixin:
'''Mixin class for transformers that generate their own names by prefixing.
This mixin is useful when the transformer needs to generate its own feature
names out, such as :class:`~sklearn.decomposition.PCA`. For example, if
:class:`~sklearn.decomposition.PCA` outp... | 2 | 2 | 21 | 3 | 5 | 13 | 1 | 5.5 | 0 | 0 | 0 | 28 | 1 | 1 | 1 | 1 | 46 | 7 | 6 | 3 | 4 | 33 | 4 | 2 | 2 | 1 | 0 | 0 | 1 |
322,443 | 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/base.py | sklearn.base.ClassifierMixin | from .utils._tags import ClassifierTags, RegressorTags, Tags, TargetTags, TransformerTags, get_tags
class ClassifierMixin:
"""Mixin class for all classifiers in scikit-learn.
This mixin defines the following functionality:
- set estimator type to `"classifier"` through the `estimator_type` tag;
- `sc... |
class ClassifierMixin:
'''Mixin class for all classifiers in scikit-learn.
This mixin defines the following functionality:
- set estimator type to `"classifier"` through the `estimator_type` tag;
- `score` method that default to :func:`~sklearn.metrics.accuracy_score`.
- enforce that `fit` requires... | 3 | 2 | 17 | 3 | 5 | 9 | 1 | 4.27 | 0 | 2 | 1 | 52 | 2 | 0 | 2 | 2 | 71 | 13 | 11 | 6 | 7 | 47 | 11 | 6 | 7 | 1 | 0 | 0 | 2 |
322,444 | 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/base.py | sklearn.base.ClusterMixin | class ClusterMixin:
"""Mixin class for all cluster estimators in scikit-learn.
- set estimator type to `"clusterer"` through the `estimator_type` tag;
- `fit_predict` method returning the cluster labels associated to each sample.
Examples
--------
>>> import numpy as np
>>> from sklearn.ba... | class ClusterMixin:
'''Mixin class for all cluster estimators in scikit-learn.
- set estimator type to `"clusterer"` through the `estimator_type` tag;
- `fit_predict` method returning the cluster labels associated to each sample.
Examples
--------
>>> import numpy as np
>>> from sklearn.base... | 3 | 2 | 16 | 3 | 5 | 9 | 2 | 3.09 | 0 | 1 | 0 | 9 | 2 | 0 | 2 | 2 | 55 | 10 | 11 | 5 | 8 | 34 | 11 | 5 | 8 | 2 | 0 | 1 | 3 |
322,445 | 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/base.py | sklearn.base.DensityMixin | class DensityMixin:
"""Mixin class for all density estimators in scikit-learn.
This mixin defines the following functionality:
- sets estimator type to `"density_estimator"` through the `estimator_type` tag;
- `score` method that default that do no-op.
Examples
--------
>>> from sklearn.b... | class DensityMixin:
'''Mixin class for all density estimators in scikit-learn.
This mixin defines the following functionality:
- sets estimator type to `"density_estimator"` through the `estimator_type` tag;
- `score` method that default that do no-op.
Examples
--------
>>> from sklearn.base... | 3 | 2 | 10 | 2 | 3 | 6 | 1 | 3.38 | 0 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 44 | 9 | 8 | 5 | 5 | 27 | 8 | 5 | 5 | 1 | 0 | 0 | 2 |
322,446 | 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/base.py | sklearn.base.MetaEstimatorMixin | class MetaEstimatorMixin:
"""Mixin class for all meta estimators in scikit-learn.
This mixin is empty, and only exists to indicate that the estimator is a
meta-estimator.
.. versionchanged:: 1.6
The `_required_parameters` is now removed and is unnecessary since tests are
refactored and... | class MetaEstimatorMixin:
'''Mixin class for all meta estimators in scikit-learn.
This mixin is empty, and only exists to indicate that the estimator is a
meta-estimator.
.. versionchanged:: 1.6
The `_required_parameters` is now removed and is unnecessary since tests are
refactored and d... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 25 | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 29 | 3 | 1 | 1 | 0 | 25 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
322,447 | 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/base.py | sklearn.base.MultiOutputMixin | class MultiOutputMixin:
"""Mixin to mark estimators that support multioutput."""
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.target_tags.multi_output = True
return tags | class MultiOutputMixin:
'''Mixin to mark estimators that support multioutput.'''
def __sklearn_tags__(self):
pass | 2 | 1 | 4 | 0 | 4 | 0 | 1 | 0.2 | 0 | 1 | 0 | 17 | 1 | 0 | 1 | 1 | 7 | 1 | 5 | 3 | 3 | 1 | 5 | 3 | 3 | 1 | 0 | 0 | 1 |
322,448 | 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/base.py | sklearn.base.OneToOneFeatureMixin | from .utils.validation import _check_feature_names_in, _generate_get_feature_names_out, _is_fitted, check_array, check_is_fitted
class OneToOneFeatureMixin:
"""Provides `get_feature_names_out` for simple transformers.
This mixin assumes there's a 1-to-1 correspondence between input features
and output fea... |
class OneToOneFeatureMixin:
'''Provides `get_feature_names_out` for simple transformers.
This mixin assumes there's a 1-to-1 correspondence between input features
and output features, such as :class:`~sklearn.preprocessing.StandardScaler`.
Examples
--------
>>> import numpy as np
>>> from s... | 2 | 2 | 25 | 3 | 3 | 19 | 1 | 8.5 | 0 | 0 | 0 | 11 | 1 | 0 | 1 | 1 | 44 | 6 | 4 | 2 | 2 | 34 | 4 | 2 | 2 | 1 | 0 | 0 | 1 |
322,449 | 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/base.py | sklearn.base.OutlierMixin | from .utils._metadata_requests import _MetadataRequester, _routing_enabled
import warnings
class OutlierMixin:
"""Mixin class for all outlier detection estimators in scikit-learn.
This mixin defines the following functionality:
- set estimator type to `"outlier_detector"` through the `estimator_type` tag... |
class OutlierMixin:
'''Mixin class for all outlier detection estimators in scikit-learn.
This mixin defines the following functionality:
- set estimator type to `"outlier_detector"` through the `estimator_type` tag;
- `fit_predict` method that default to `fit` and `predict`.
Examples
--------
... | 3 | 2 | 28 | 4 | 12 | 13 | 2 | 1.8 | 0 | 2 | 0 | 7 | 2 | 0 | 2 | 2 | 83 | 13 | 25 | 6 | 22 | 45 | 12 | 6 | 9 | 3 | 0 | 2 | 4 |
322,450 | 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/base.py | sklearn.base.RegressorMixin | from .utils._tags import ClassifierTags, RegressorTags, Tags, TargetTags, TransformerTags, get_tags
class RegressorMixin:
"""Mixin class for all regression estimators in scikit-learn.
This mixin defines the following functionality:
- set estimator type to `"regressor"` through the `estimator_type` tag;
... |
class RegressorMixin:
'''Mixin class for all regression estimators in scikit-learn.
This mixin defines the following functionality:
- set estimator type to `"regressor"` through the `estimator_type` tag;
- `score` method that default to :func:`~sklearn.metrics.r2_score`.
- enforce that `fit` requir... | 3 | 2 | 26 | 4 | 5 | 17 | 1 | 5.17 | 0 | 2 | 1 | 49 | 2 | 0 | 2 | 2 | 89 | 15 | 12 | 7 | 8 | 62 | 12 | 7 | 8 | 1 | 0 | 0 | 2 |
322,451 | 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/base.py | sklearn.base.TransformerMixin | import warnings
from .utils._tags import ClassifierTags, RegressorTags, Tags, TargetTags, TransformerTags, get_tags
from .utils._set_output import _SetOutputMixin
from .utils._metadata_requests import _MetadataRequester, _routing_enabled
class TransformerMixin(_SetOutputMixin):
"""Mixin class for all transformers ... |
class TransformerMixin(_SetOutputMixin):
'''Mixin class for all transformers in scikit-learn.
This mixin defines the following functionality:
- a `fit_transform` method that delegates to `fit` and `transform`;
- a `set_output` method to output `X` as a specific container type.
If :term:`get_feature... | 3 | 2 | 32 | 4 | 13 | 15 | 3 | 2.07 | 1 | 3 | 1 | 81 | 2 | 0 | 2 | 4 | 97 | 14 | 27 | 5 | 24 | 56 | 13 | 5 | 10 | 4 | 1 | 2 | 5 |
322,452 | 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/base.py | sklearn.base._UnstableArchMixin | from .utils.fixes import _IS_32BIT
import platform
class _UnstableArchMixin:
"""Mark estimators that are non-determinstic on 32bit or PowerPC"""
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.non_deterministic = _IS_32BIT or platform.machine().startswith(('ppc', 'powerpc'))... |
class _UnstableArchMixin:
'''Mark estimators that are non-determinstic on 32bit or PowerPC'''
def __sklearn_tags__(self):
pass | 2 | 1 | 6 | 0 | 6 | 0 | 1 | 0.14 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 9 | 1 | 7 | 3 | 5 | 1 | 5 | 3 | 3 | 1 | 0 | 0 | 1 |
322,453 | 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/calibration.py | sklearn.calibration.CalibratedClassifierCV | from .utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
from .svm import LinearSVC
from .utils._response import _get_response_values, _process_predict_proba
from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone
from inspect... |
class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
'''Probability calibration with isotonic regression or logistic regression.
This class uses cross-validation to both estimate the parameters of a
classifier and subsequently calibrate a classifier. With
`ensemble=True`, f... | 9 | 6 | 42 | 4 | 28 | 10 | 4 | 1.06 | 3 | 13 | 8 | 0 | 7 | 9 | 7 | 40 | 509 | 72 | 213 | 50 | 194 | 225 | 96 | 39 | 88 | 18 | 2 | 4 | 27 |
322,454 | 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/calibration.py | sklearn.calibration.CalibrationDisplay | from .utils._plotting import _BinaryClassifierCurveDisplayMixin, _validate_style_kwargs
class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
"""Calibration curve (also known as reliability diagram) visualization.
It is recommended to use
:func:`~sklearn.calibration.CalibrationDisplay.from_estimat... |
class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
'''Calibration curve (also known as reliability diagram) visualization.
It is recommended to use
:func:`~sklearn.calibration.CalibrationDisplay.from_estimator` or
:func:`~sklearn.calibration.CalibrationDisplay.from_predictions`
to create... | 7 | 4 | 74 | 13 | 22 | 39 | 2 | 2.39 | 1 | 0 | 0 | 0 | 2 | 8 | 4 | 10 | 376 | 71 | 90 | 52 | 56 | 215 | 32 | 23 | 27 | 4 | 1 | 1 | 7 |
322,455 | 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/calibration.py | sklearn.calibration._CalibratedClassifier | from .utils._response import _get_response_values, _process_predict_proba
import numpy as np
from .preprocessing import LabelEncoder, label_binarize
from .utils.validation import _check_method_params, _check_pos_label_consistency, _check_response_method, _check_sample_weight, _num_samples, check_consistent_length, chec... |
class _CalibratedClassifier:
'''Pipeline-like chaining a fitted classifier and its fitted calibrators.
Parameters
----------
estimator : estimator instance
Fitted classifier.
calibrators : list of fitted estimator instances
List of fitted calibrators (either 'IsotonicRegression' or
... | 3 | 2 | 31 | 5 | 17 | 10 | 3 | 1.09 | 0 | 2 | 1 | 0 | 2 | 4 | 2 | 2 | 86 | 15 | 34 | 15 | 31 | 37 | 25 | 15 | 22 | 5 | 0 | 2 | 6 |
322,456 | 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/calibration.py | sklearn.calibration._SigmoidCalibration | from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone
from .utils import _safe_indexing, column_or_1d, get_tags, indexable
from scipy.special import expit
class _SigmoidCalibration(RegressorMixin, BaseEstimator):
"""Sigmoid regression model.
Attributes
-... |
class _SigmoidCalibration(RegressorMixin, BaseEstimator):
'''Sigmoid regression model.
Attributes
----------
a_ : float
The slope.
b_ : float
The intercept.
'''
def fit(self, X, y, sample_weight=None):
'''Fit the model using X, y as training data.
Parameters... | 3 | 3 | 20 | 4 | 5 | 12 | 1 | 3.2 | 2 | 0 | 0 | 0 | 2 | 2 | 2 | 35 | 53 | 11 | 10 | 4 | 7 | 32 | 10 | 4 | 7 | 1 | 2 | 0 | 2 |
322,457 | 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/cluster/_affinity_propagation.py | sklearn.cluster._affinity_propagation.AffinityPropagation | from ..base import BaseEstimator, ClusterMixin, _fit_context
from ..utils import check_random_state
import numpy as np
from ..exceptions import ConvergenceWarning
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted, validate_data
from numbers import... |
class AffinityPropagation(ClusterMixin, BaseEstimator):
'''Perform Affinity Propagation Clustering of data.
Read more in the :ref:`User Guide <affinity_propagation>`.
Parameters
----------
damping : float, default=0.5
Damping factor in the range `[0.5, 1.0)` is the extent to
which t... | 7 | 4 | 28 | 3 | 16 | 8 | 2 | 1.52 | 2 | 3 | 1 | 0 | 5 | 13 | 5 | 38 | 296 | 50 | 98 | 35 | 80 | 149 | 45 | 21 | 39 | 5 | 2 | 2 | 11 |
322,458 | 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/cluster/_agglomerative.py | sklearn.cluster._agglomerative.AgglomerativeClustering | from . import _hierarchical_fast as _hierarchical
import numpy as np
from numbers import Integral, Real
from ..metrics.pairwise import _VALID_METRICS, paired_distances
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, _fit_context
from ..utils import check_array
from ..utils.validation im... |
class AgglomerativeClustering(ClusterMixin, BaseEstimator):
'''
Agglomerative Clustering.
Recursively merges pair of clusters of sample data; uses linkage distance.
Read more in the :ref:`User Guide <hierarchical_clustering>`.
Parameters
----------
n_clusters : int or None, default=2
... | 6 | 4 | 41 | 6 | 23 | 13 | 4 | 1.59 | 2 | 2 | 0 | 1 | 4 | 14 | 4 | 37 | 338 | 60 | 108 | 41 | 91 | 172 | 60 | 29 | 55 | 14 | 2 | 2 | 17 |
322,459 | 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/cluster/_agglomerative.py | sklearn.cluster._agglomerative.FeatureAgglomeration | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, _fit_context
from ..metrics.pairwise import _VALID_METRICS, paired_distances
from ._feature_agglomeration import AgglomerationTransform
from ..utils._param_validation import HasMethods, Interval, StrOptions, validate_params
import numpy as... |
class FeatureAgglomeration(ClassNamePrefixFeaturesOutMixin, AgglomerationTransform, AgglomerativeClustering):
'''Agglomerate features.
Recursively merges pair of clusters of features.
Refer to
:ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`
for an example... | 6 | 3 | 16 | 1 | 10 | 4 | 1 | 2.58 | 3 | 2 | 0 | 0 | 3 | 2 | 3 | 47 | 215 | 36 | 50 | 23 | 30 | 129 | 12 | 7 | 8 | 1 | 3 | 0 | 3 |
322,460 | 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/cluster/_bicluster.py | sklearn.cluster._bicluster.BaseSpectral | from ..utils.extmath import _randomized_svd, make_nonnegative, safe_sparse_dot
import numpy as np
from ..base import BaseEstimator, BiclusterMixin, _fit_context
from ..utils.validation import assert_all_finite, validate_data
from numbers import Integral
from ..utils._param_validation import Interval, StrOptions
from ._... |
class BaseSpectral(BiclusterMixin, BaseEstimator, metaclass=ABCMeta):
'''Base class for spectral biclustering.'''
@abstractmethod
def __init__(self, n_clusters=3, svd_method='randomized', n_svd_vecs=None, mini_batch=False, init='k-means++', n_init=10, random_state=None):
pass
@abstractmethod
... | 10 | 4 | 16 | 1 | 12 | 4 | 2 | 0.26 | 3 | 3 | 2 | 2 | 6 | 7 | 6 | 61 | 118 | 12 | 84 | 37 | 65 | 22 | 51 | 25 | 44 | 6 | 3 | 2 | 12 |
322,461 | 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/cluster/_bicluster.py | sklearn.cluster._bicluster.SpectralBiclustering | from ..utils.extmath import _randomized_svd, make_nonnegative, safe_sparse_dot
import numpy as np
from numbers import Integral
from ..utils import check_random_state, check_scalar
from ..utils._param_validation import Interval, StrOptions
from scipy.linalg import norm
class SpectralBiclustering(BaseSpectral):
"""S... |
class SpectralBiclustering(BaseSpectral):
'''Spectral biclustering (Kluger, 2003).
Partitions rows and columns under the assumption that the data has
an underlying checkerboard structure. For instance, if there are
two row partitions and three column partitions, each row will
belong to three biclus... | 7 | 3 | 21 | 2 | 18 | 1 | 3 | 0.93 | 1 | 5 | 0 | 0 | 5 | 7 | 5 | 66 | 262 | 45 | 113 | 46 | 93 | 105 | 53 | 32 | 46 | 6 | 4 | 2 | 15 |
322,462 | 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/cluster/_bicluster.py | sklearn.cluster._bicluster.SpectralCoclustering | from numbers import Integral
import numpy as np
from ..utils._param_validation import Interval, StrOptions
class SpectralCoclustering(BaseSpectral):
"""Spectral Co-Clustering algorithm (Dhillon, 2001).
Clusters rows and columns of an array `X` to solve the relaxed
normalized cut of the bipartite graph cre... |
class SpectralCoclustering(BaseSpectral):
'''Spectral Co-Clustering algorithm (Dhillon, 2001).
Clusters rows and columns of an array `X` to solve the relaxed
normalized cut of the bipartite graph created from `X` as follows:
the edge between row vertex `i` and column vertex `j` has weight
`X[i, j]`... | 4 | 1 | 12 | 1 | 11 | 0 | 1 | 2.26 | 1 | 4 | 0 | 0 | 3 | 4 | 3 | 64 | 156 | 32 | 38 | 25 | 24 | 86 | 18 | 15 | 14 | 2 | 4 | 1 | 4 |
322,463 | 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/cluster/_birch.py | sklearn.cluster._birch.Birch | from scipy import sparse
from numbers import Integral, Real
import numpy as np
from ..utils._param_validation import Hidden, Interval, StrOptions
from ..metrics import pairwise_distances_argmin
import warnings
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, _fit_contex... |
class Birch(ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, BaseEstimator):
'''Implements the BIRCH clustering algorithm.
It is a memory-efficient, online-learning algorithm provided as an
alternative to :class:`MiniBatchKMeans`. It constructs a tree
data structure with the cluster cen... | 13 | 8 | 24 | 3 | 14 | 8 | 2 | 1.18 | 4 | 7 | 4 | 0 | 10 | 12 | 10 | 48 | 391 | 68 | 148 | 56 | 125 | 175 | 89 | 44 | 78 | 6 | 2 | 2 | 22 |
322,464 | 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/cluster/_birch.py | sklearn.cluster._birch._CFNode | import numpy as np
class _CFNode:
"""Each node in a CFTree is called a CFNode.
The CFNode can have a maximum of branching_factor
number of CFSubclusters.
Parameters
----------
threshold : float
Threshold needed for a new subcluster to enter a CFSubcluster.
branching_factor : int
... |
class _CFNode:
'''Each node in a CFTree is called a CFNode.
The CFNode can have a maximum of branching_factor
number of CFSubclusters.
Parameters
----------
threshold : float
Threshold needed for a new subcluster to enter a CFSubcluster.
branching_factor : int
Maximum number... | 5 | 3 | 26 | 3 | 18 | 6 | 3 | 0.77 | 0 | 0 | 0 | 0 | 4 | 11 | 4 | 4 | 153 | 27 | 71 | 26 | 66 | 55 | 57 | 26 | 52 | 7 | 0 | 3 | 10 |
322,465 | 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/cluster/_birch.py | sklearn.cluster._birch._CFSubcluster | from math import sqrt
import numpy as np
class _CFSubcluster:
"""Each subcluster in a CFNode is called a CFSubcluster.
A CFSubcluster can have a CFNode has its child.
Parameters
----------
linear_sum : ndarray of shape (n_features,), default=None
Sample. This is kept optional to allow ini... |
class _CFSubcluster:
'''Each subcluster in a CFNode is called a CFSubcluster.
A CFSubcluster can have a CFNode has its child.
Parameters
----------
linear_sum : ndarray of shape (n_features,), default=None
Sample. This is kept optional to allow initialization of empty
subclusters.
... | 6 | 3 | 13 | 1 | 10 | 3 | 2 | 0.97 | 0 | 0 | 0 | 0 | 4 | 6 | 4 | 4 | 93 | 14 | 40 | 18 | 34 | 39 | 30 | 16 | 25 | 2 | 0 | 1 | 6 |
322,466 | 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/cluster/_bisect_k_means.py | sklearn.cluster._bisect_k_means.BisectingKMeans | from ..utils.validation import _check_sample_weight, check_is_fitted, check_random_state, validate_data
from ._kmeans import _BaseKMeans, _kmeans_single_elkan, _kmeans_single_lloyd, _labels_inertia_threadpool_limit
from ..base import _fit_context
import numpy as np
import warnings
from ..utils._param_validation import ... |
class BisectingKMeans(_BaseKMeans):
'''Bisecting K-Means clustering.
Read more in the :ref:`User Guide <bisect_k_means>`.
.. versionadded:: 1.1
Parameters
----------
n_clusters : int, default=8
The number of clusters to form as well as the number of
centroids to generate.
in... | 10 | 7 | 39 | 7 | 20 | 12 | 3 | 1.21 | 1 | 4 | 1 | 0 | 8 | 17 | 8 | 80 | 461 | 92 | 169 | 64 | 146 | 204 | 90 | 44 | 81 | 7 | 5 | 2 | 22 |
322,467 | 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/cluster/_bisect_k_means.py | sklearn.cluster._bisect_k_means._BisectingTree | class _BisectingTree:
"""Tree structure representing the hierarchical clusters of BisectingKMeans."""
def __init__(self, center, indices, score):
"""Create a new cluster node in the tree.
The node holds the center of this cluster and the indices of the data points
that belong to it.
... | class _BisectingTree:
'''Tree structure representing the hierarchical clusters of BisectingKMeans.'''
def __init__(self, center, indices, score):
'''Create a new cluster node in the tree.
The node holds the center of this cluster and the indices of the data points
that belong to it.
... | 5 | 5 | 11 | 2 | 7 | 3 | 2 | 0.46 | 0 | 0 | 0 | 0 | 4 | 5 | 4 | 4 | 51 | 10 | 28 | 13 | 23 | 13 | 23 | 13 | 18 | 3 | 0 | 2 | 7 |
322,468 | 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/cluster/_dbscan.py | sklearn.cluster._dbscan.DBSCAN | from ..neighbors import NearestNeighbors
from ..utils._param_validation import Interval, StrOptions, validate_params
from ._dbscan_inner import dbscan_inner
from scipy import sparse
from ..base import BaseEstimator, ClusterMixin, _fit_context
from ..utils.validation import _check_sample_weight, validate_data
from ..met... |
class DBSCAN(ClusterMixin, BaseEstimator):
'''Perform DBSCAN clustering from vector array or distance matrix.
DBSCAN - Density-Based Spatial Clustering of Applications with Noise.
Finds core samples of high density and expands clusters from them.
Good for data which contains clusters of similar density... | 6 | 3 | 33 | 4 | 16 | 13 | 2 | 2.09 | 2 | 3 | 1 | 0 | 4 | 11 | 4 | 37 | 300 | 51 | 81 | 37 | 62 | 169 | 43 | 23 | 38 | 5 | 2 | 2 | 8 |
322,469 | 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/cluster/_feature_agglomeration.py | sklearn.cluster._feature_agglomeration.AgglomerationTransform | import numpy as np
from ..utils.validation import check_is_fitted, validate_data
from ..base import TransformerMixin
from scipy.sparse import issparse
class AgglomerationTransform(TransformerMixin):
"""
A class for feature agglomeration via the transform interface.
"""
def transform(self, X):
... |
class AgglomerationTransform(TransformerMixin):
'''
A class for feature agglomeration via the transform interface.
'''
def transform(self, X):
'''
Transform a new matrix using the built clustering.
Parameters
----------
X : array-like of shape (n_samples, n_... | 3 | 3 | 26 | 3 | 10 | 13 | 2 | 1.38 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 6 | 58 | 8 | 21 | 7 | 18 | 29 | 15 | 7 | 12 | 2 | 2 | 1 | 3 |
322,470 | 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/cluster/_hdbscan/hdbscan.py | sklearn.cluster._hdbscan.hdbscan.HDBSCAN | from scipy.sparse import csgraph, issparse
from ...utils._param_validation import Interval, StrOptions
import numpy as np
from ...base import BaseEstimator, ClusterMixin, _fit_context
from ...metrics import pairwise_distances
from ._tree import HIERARCHY_dtype, labelling_at_cut, tree_to_labels
from ...utils.validation ... |
class HDBSCAN(ClusterMixin, BaseEstimator):
'''Cluster data using hierarchical density-based clustering.
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications
with Noise. Performs :class:`~sklearn.cluster.DBSCAN` over varying epsilon
values and integrates the result to find a clusteri... | 8 | 5 | 56 | 6 | 34 | 17 | 5 | 1.2 | 2 | 6 | 0 | 0 | 6 | 22 | 6 | 39 | 582 | 79 | 229 | 71 | 203 | 274 | 124 | 52 | 117 | 21 | 2 | 2 | 31 |
322,471 | 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/cluster/_kmeans.py | sklearn.cluster._kmeans.KMeans | from ..utils._openmp_helpers import _openmp_effective_n_threads
from ._k_means_common import CHUNK_SIZE, _inertia_dense, _inertia_sparse, _is_same_clustering
from ..utils import check_array, check_random_state
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, _fit_contex... |
class KMeans(_BaseKMeans):
'''K-Means clustering.
Read more in the :ref:`User Guide <k_means>`.
Parameters
----------
n_clusters : int, default=8
The number of clusters to form as well as the number of
centroids to generate.
For an example of how to choose an optimal value f... | 6 | 3 | 44 | 5 | 30 | 8 | 4 | 1.36 | 1 | 5 | 1 | 0 | 4 | 13 | 4 | 76 | 369 | 69 | 127 | 45 | 109 | 173 | 59 | 28 | 54 | 11 | 5 | 2 | 15 |
322,472 | 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/cluster/_kmeans.py | sklearn.cluster._kmeans.MiniBatchKMeans | from ..utils.extmath import row_norms, stable_cumsum
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils import check_array, check_random_state
import numpy as np
from ..utils.validation import _check_sample_weight, _is_arraylike_not_scalar, check_is_fitted, validate_data
import war... |
class MiniBatchKMeans(_BaseKMeans):
'''
Mini-Batch K-Means clustering.
Read more in the :ref:`User Guide <mini_batch_kmeans>`.
Parameters
----------
n_clusters : int, default=8
The number of clusters to form as well as the number of
centroids to generate.
init : {'k-means++'... | 10 | 6 | 58 | 8 | 38 | 12 | 5 | 0.87 | 1 | 5 | 0 | 0 | 7 | 22 | 7 | 79 | 626 | 110 | 276 | 71 | 249 | 240 | 135 | 51 | 127 | 10 | 5 | 3 | 33 |
322,473 | 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/cluster/_kmeans.py | sklearn.cluster._kmeans._BaseKMeans | from ..utils import check_array, check_random_state
from ..metrics.pairwise import _euclidean_distances, euclidean_distances
from numbers import Integral, Real
from ._k_means_common import CHUNK_SIZE, _inertia_dense, _inertia_sparse, _is_same_clustering
import numpy as np
from abc import ABC, abstractmethod
import scip... |
class _BaseKMeans(ClassNamePrefixFeaturesOutMixin, TransformerMixin, ClusterMixin, BaseEstimator, ABC):
'''Base class for KMeans and MiniBatchKMeans'''
def __init__(self, n_clusters, *, init, n_init, max_iter, tol, verbose, random_state):
pass
def _check_params_vs_input(self, X, default_n_init=No... | 16 | 11 | 23 | 3 | 11 | 9 | 2 | 0.69 | 5 | 5 | 0 | 3 | 14 | 11 | 14 | 72 | 353 | 59 | 175 | 62 | 138 | 120 | 89 | 38 | 74 | 8 | 4 | 2 | 32 |
322,474 | 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/cluster/_mean_shift.py | sklearn.cluster._mean_shift.MeanShift | from .._config import config_context
from ..utils._param_validation import Interval, validate_params
from numbers import Integral, Real
from ..metrics.pairwise import pairwise_distances_argmin
import numpy as np
from ..utils.parallel import Parallel, delayed
from ..utils.validation import check_is_fitted, validate_data... |
class MeanShift(ClusterMixin, BaseEstimator):
'''Mean shift clustering using a flat kernel.
Mean shift clustering aims to discover "blobs" in a smooth density of
samples. It is a centroid-based algorithm, which works by updating
candidates for centroids to be the mean of the points within a given
r... | 5 | 3 | 42 | 5 | 27 | 12 | 4 | 1.53 | 2 | 7 | 2 | 0 | 3 | 10 | 3 | 36 | 280 | 52 | 91 | 41 | 76 | 139 | 55 | 30 | 51 | 10 | 2 | 2 | 12 |
322,475 | 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/cluster/_optics.py | sklearn.cluster._optics.OPTICS | from ..exceptions import DataConversionWarning
import warnings
from ..metrics.pairwise import _VALID_METRICS, PAIRWISE_BOOLEAN_FUNCTIONS
from numbers import Integral, Real
from ..utils.validation import check_memory, validate_data
from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions, valid... |
class OPTICS(ClusterMixin, BaseEstimator):
'''Estimate clustering structure from vector array.
OPTICS (Ordering Points To Identify the Clustering Structure), closely
related to DBSCAN, finds core samples of high density and expands clusters
from them [1]_. Unlike DBSCAN, it keeps cluster hierarchy for ... | 4 | 2 | 62 | 5 | 46 | 11 | 5 | 1.59 | 2 | 6 | 2 | 0 | 2 | 20 | 2 | 35 | 362 | 55 | 119 | 49 | 96 | 189 | 41 | 26 | 38 | 8 | 2 | 2 | 9 |
322,476 | 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/cluster/_spectral.py | sklearn.cluster._spectral.SpectralClustering | from ..utils.validation import validate_data
from ..neighbors import NearestNeighbors, kneighbors_graph
import warnings
from ..manifold._spectral_embedding import _spectral_embedding
from numbers import Integral, Real
import numpy as np
from ..utils import as_float_array, check_random_state
from ..base import BaseEstim... |
class SpectralClustering(ClusterMixin, BaseEstimator):
'''Apply clustering to a projection of the normalized Laplacian.
In practice Spectral Clustering is very useful when the structure of
the individual clusters is highly non-convex, or more generally when
a measure of the center and spread of the clu... | 6 | 3 | 42 | 3 | 29 | 11 | 4 | 1.56 | 2 | 2 | 1 | 0 | 4 | 17 | 4 | 37 | 427 | 63 | 142 | 50 | 118 | 222 | 55 | 31 | 50 | 11 | 2 | 2 | 14 |
322,477 | 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/compose/_column_transformer.py | sklearn.compose._column_transformer.ColumnTransformer | from ..utils._set_output import _get_container_adapter, _get_output_config, _safe_set_output
from ..utils._repr_html.estimator import _VisualBlock
from collections import Counter
from ..utils._tags import get_tags
from functools import partial
from itertools import chain
from numbers import Integral, Real
from ..utils.... | null | 35 | 24 | 33 | 4 | 19 | 10 | 4 | 0.85 | 2 | 25 | 6 | 0 | 30 | 14 | 30 | 90 | 1,265 | 179 | 588 | 160 | 539 | 501 | 332 | 129 | 300 | 13 | 4 | 6 | 114 |
322,478 | 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/compose/_column_transformer.py | sklearn.compose._column_transformer.make_column_selector | class make_column_selector:
"""Create a callable to select columns to be used with
:class:`ColumnTransformer`.
:func:`make_column_selector` can select columns based on datatype or the
columns name with a regex. When using multiple selection criteria, **all**
criteria must match for a column to be s... | class make_column_selector:
'''Create a callable to select columns to be used with
:class:`ColumnTransformer`.
:func:`make_column_selector` can select columns based on datatype or the
columns name with a regex. When using multiple selection criteria, **all**
criteria must match for a column to be se... | 3 | 2 | 13 | 1 | 9 | 4 | 3 | 3 | 0 | 1 | 0 | 0 | 2 | 3 | 2 | 2 | 87 | 11 | 19 | 8 | 16 | 57 | 15 | 8 | 12 | 4 | 0 | 1 | 5 |
322,479 | 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/compose/_target.py | sklearn.compose._target.TransformedTargetRegressor | from ..utils._metadata_requests import MetadataRouter, MethodMapping, _routing_enabled, process_routing
from ..utils.validation import check_is_fitted
from ..base import BaseEstimator, RegressorMixin, _fit_context, clone
import warnings
import numpy as np
from ..utils._tags import get_tags
from ..utils import Bunch, _s... |
class TransformedTargetRegressor(RegressorMixin, BaseEstimator):
'''Meta-estimator to regress on a transformed target.
Useful for applying a non-linear transformation to the target `y` in
regression problems. This transformation can be given as a Transformer
such as the :class:`~sklearn.preprocessing.Q... | 11 | 6 | 30 | 4 | 17 | 9 | 3 | 1.05 | 2 | 11 | 6 | 0 | 8 | 9 | 8 | 41 | 372 | 63 | 151 | 45 | 130 | 158 | 76 | 32 | 67 | 7 | 2 | 2 | 25 |
322,480 | 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/covariance/_elliptic_envelope.py | sklearn.covariance._elliptic_envelope.EllipticEnvelope | import numpy as np
from ..utils.validation import check_is_fitted
from ..base import OutlierMixin, _fit_context
from ..utils._param_validation import Interval
from numbers import Real
from ._robust_covariance import MinCovDet
from ..metrics import accuracy_score
class EllipticEnvelope(OutlierMixin, MinCovDet):
"""... |
class EllipticEnvelope(OutlierMixin, MinCovDet):
'''An object for detecting outliers in a Gaussian distributed dataset.
Read more in the :ref:`User Guide <outlier_detection>`.
Parameters
----------
store_precision : bool, default=True
Specify if the estimated precision is stored.
assume... | 8 | 6 | 19 | 3 | 6 | 11 | 1 | 4.15 | 2 | 2 | 0 | 0 | 6 | 2 | 6 | 50 | 252 | 46 | 40 | 22 | 24 | 166 | 23 | 13 | 16 | 1 | 4 | 0 | 6 |
322,481 | 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/covariance/_empirical_covariance.py | sklearn.covariance._empirical_covariance.EmpiricalCovariance | import numpy as np
from ..base import BaseEstimator, _fit_context
from ..utils.validation import validate_data
from scipy import linalg
from .. import config_context
from sklearn.utils import metadata_routing
from ..utils import check_array
from ..metrics.pairwise import pairwise_distances
class EmpiricalCovariance(Ba... |
class EmpiricalCovariance(BaseEstimator):
'''Maximum likelihood covariance estimator.
Read more in the :ref:`User Guide <covariance>`.
Parameters
----------
store_precision : bool, default=True
Specifies if the estimated precision is stored.
assume_centered : bool, default=False
... | 9 | 7 | 24 | 3 | 8 | 13 | 2 | 2.4 | 1 | 1 | 0 | 5 | 7 | 5 | 7 | 38 | 255 | 44 | 62 | 25 | 53 | 149 | 48 | 24 | 40 | 5 | 2 | 1 | 14 |
322,482 | 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/covariance/_graph_lasso.py | sklearn.covariance._graph_lasso.BaseGraphicalLasso | import numpy as np
from numbers import Integral, Real
from ..utils._param_validation import Interval, StrOptions, validate_params
from . import EmpiricalCovariance, empirical_covariance, log_likelihood
class BaseGraphicalLasso(EmpiricalCovariance):
_parameter_constraints: dict = {**EmpiricalCovariance._parameter_c... |
class BaseGraphicalLasso(EmpiricalCovariance):
def __init__(self, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', verbose=False, eps=np.finfo(np.float64).eps, assume_centered=False):
pass | 2 | 0 | 17 | 0 | 17 | 0 | 1 | 0 | 1 | 2 | 0 | 2 | 1 | 6 | 1 | 39 | 29 | 1 | 28 | 18 | 17 | 0 | 11 | 9 | 9 | 1 | 3 | 0 | 1 |
322,483 | 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/covariance/_graph_lasso.py | sklearn.covariance._graph_lasso.GraphicalLasso | from ..base import _fit_context
from . import EmpiricalCovariance, empirical_covariance, log_likelihood
from ..utils._param_validation import Interval, StrOptions, validate_params
import numpy as np
from ..utils.validation import _is_arraylike_not_scalar, check_random_state, check_scalar, validate_data
from numbers imp... |
class GraphicalLasso(BaseGraphicalLasso):
'''Sparse inverse covariance estimation with an l1-penalized estimator.
For a usage example see
:ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
.. versionchanged:: v0.20
... | 4 | 2 | 33 | 3 | 24 | 7 | 2 | 1.93 | 1 | 2 | 0 | 0 | 2 | 12 | 2 | 41 | 192 | 34 | 54 | 27 | 38 | 104 | 17 | 9 | 14 | 3 | 4 | 2 | 4 |
322,484 | 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/covariance/_graph_lasso.py | sklearn.covariance._graph_lasso.GraphicalLassoCV | from numbers import Integral, Real
from ..base import _fit_context
import time
from ..model_selection import check_cv, cross_val_score
from ..utils.validation import _is_arraylike_not_scalar, check_random_state, check_scalar, validate_data
import operator
from ..utils import Bunch
from ..exceptions import ConvergenceWa... |
class GraphicalLassoCV(BaseGraphicalLasso):
'''Sparse inverse covariance w/ cross-validated choice of the l1 penalty.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
.. versionchanged:: v0.20
GraphLassoCV has been rename... | 5 | 3 | 77 | 9 | 51 | 17 | 6 | 1.18 | 1 | 15 | 5 | 0 | 3 | 16 | 3 | 42 | 431 | 75 | 163 | 55 | 144 | 193 | 84 | 36 | 80 | 16 | 4 | 3 | 18 |
322,485 | 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/covariance/_robust_covariance.py | sklearn.covariance._robust_covariance.MinCovDet | import numpy as np
from ..base import _fit_context
import warnings
from ..utils import check_array, check_random_state
from scipy.stats import chi2
from scipy import linalg
from ._empirical_covariance import EmpiricalCovariance, empirical_covariance
from numbers import Integral, Real
from ..utils.validation import vali... |
class MinCovDet(EmpiricalCovariance):
'''Minimum Covariance Determinant (MCD): robust estimator of covariance.
The Minimum Covariance Determinant covariance estimator is to be applied
on Gaussian-distributed data, but could still be relevant on data
drawn from a unimodal, symmetric distribution. It is ... | 6 | 4 | 39 | 4 | 18 | 16 | 2 | 2.11 | 1 | 2 | 0 | 1 | 4 | 10 | 4 | 42 | 294 | 45 | 80 | 39 | 67 | 169 | 53 | 31 | 48 | 3 | 3 | 1 | 8 |
322,486 | 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/covariance/_shrunk_covariance.py | sklearn.covariance._shrunk_covariance.LedoitWolf | from ..utils._param_validation import Interval, validate_params
from . import EmpiricalCovariance, empirical_covariance
from ..base import _fit_context
import numpy as np
from numbers import Integral, Real
from ..utils.validation import validate_data
class LedoitWolf(EmpiricalCovariance):
"""LedoitWolf Estimator.
... |
class LedoitWolf(EmpiricalCovariance):
'''LedoitWolf Estimator.
Ledoit-Wolf is a particular form of shrinkage, where the shrinkage
coefficient is computed using O. Ledoit and M. Wolf's formula as
described in "A Well-Conditioned Estimator for Large-Dimensional
Covariance Matrices", Ledoit and Wolf,... | 4 | 2 | 18 | 2 | 9 | 8 | 2 | 4.26 | 1 | 1 | 0 | 0 | 2 | 3 | 2 | 40 | 147 | 26 | 23 | 9 | 19 | 98 | 14 | 8 | 11 | 2 | 3 | 1 | 3 |
322,487 | 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/covariance/_shrunk_covariance.py | sklearn.covariance._shrunk_covariance.OAS | from ..utils.validation import validate_data
from ..base import _fit_context
import numpy as np
from . import EmpiricalCovariance, empirical_covariance
class OAS(EmpiricalCovariance):
"""Oracle Approximating Shrinkage Estimator.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
-----... |
class OAS(EmpiricalCovariance):
'''Oracle Approximating Shrinkage Estimator.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
store_precision : bool, default=True
Specify if the estimated precision is stored.
assume_centered : bool, default=False
If ... | 3 | 2 | 29 | 4 | 10 | 15 | 2 | 8.25 | 1 | 0 | 0 | 0 | 1 | 2 | 1 | 39 | 135 | 24 | 12 | 6 | 9 | 99 | 10 | 5 | 8 | 2 | 3 | 1 | 2 |
322,488 | 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/covariance/_shrunk_covariance.py | sklearn.covariance._shrunk_covariance.ShrunkCovariance | from ..utils._param_validation import Interval, validate_params
from ..utils.validation import validate_data
from numbers import Integral, Real
from ..base import _fit_context
from . import EmpiricalCovariance, empirical_covariance
import numpy as np
class ShrunkCovariance(EmpiricalCovariance):
"""Covariance estim... |
class ShrunkCovariance(EmpiricalCovariance):
'''Covariance estimator with shrinkage.
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
store_precision : bool, default=True
Specify if the estimated precision is stored.
assume_centered : bool, default=False
... | 4 | 2 | 17 | 2 | 8 | 8 | 2 | 3.86 | 1 | 1 | 0 | 0 | 2 | 2 | 2 | 40 | 125 | 23 | 21 | 8 | 17 | 81 | 14 | 7 | 11 | 2 | 3 | 1 | 3 |
322,489 | 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/cross_decomposition/_pls.py | sklearn.cross_decomposition._pls.CCA | class CCA(_PLS):
"""Canonical Correlation Analysis, also known as "Mode B" PLS.
For a comparison between other cross decomposition algorithms, see
:ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`.
Read more in the :ref:`User Guide <cross_decomposition>`.
Param... | class CCA(_PLS):
'''Canonical Correlation Analysis, also known as "Mode B" PLS.
For a comparison between other cross decomposition algorithms, see
:ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`.
Read more in the :ref:`User Guide <cross_decomposition>`.
Paramete... | 2 | 1 | 13 | 0 | 13 | 0 | 1 | 3.94 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 67 | 108 | 24 | 17 | 6 | 13 | 67 | 6 | 4 | 4 | 1 | 4 | 1 | 1 |
322,490 | 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/cross_decomposition/_pls.py | sklearn.cross_decomposition._pls.PLSCanonical | class PLSCanonical(_PLS):
"""Partial Least Squares transformer and regressor.
For a comparison between other cross decomposition algorithms, see
:ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`.
Read more in the :ref:`User Guide <cross_decomposition>`.
.. vers... | class PLSCanonical(_PLS):
'''Partial Least Squares transformer and regressor.
For a comparison between other cross decomposition algorithms, see
:ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`.
Read more in the :ref:`User Guide <cross_decomposition>`.
.. version... | 2 | 1 | 20 | 0 | 20 | 0 | 1 | 3.33 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 67 | 131 | 27 | 24 | 13 | 13 | 80 | 6 | 4 | 4 | 1 | 4 | 1 | 1 |
322,491 | 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/cross_decomposition/_pls.py | sklearn.cross_decomposition._pls.PLSRegression | class PLSRegression(_PLS):
"""PLS regression.
PLSRegression is also known as PLS2 or PLS1, depending on the number of
targets.
For a comparison between other cross decomposition algorithms, see
:ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`.
Read more in... | class PLSRegression(_PLS):
'''PLS regression.
PLSRegression is also known as PLS2 or PLS1, depending on the number of
targets.
For a comparison between other cross decomposition algorithms, see
:ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`.
Read more in th... | 3 | 2 | 18 | 2 | 9 | 8 | 1 | 4.32 | 1 | 1 | 0 | 0 | 2 | 2 | 2 | 68 | 151 | 34 | 22 | 9 | 17 | 95 | 11 | 7 | 8 | 1 | 4 | 1 | 2 |
322,492 | 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/cross_decomposition/_pls.py | sklearn.cross_decomposition._pls.PLSSVD | import numpy as np
from ..utils.extmath import svd_flip
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, MultiOutputMixin, RegressorMixin, TransformerMixin, _fit_context
from ..utils.validation import FLOAT_DTYPES, check_is_fitted, validate_data
from ..utils import check_array, check_consistent_length... |
class PLSSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Partial Least Square SVD.
This transformer simply performs a SVD on the cross-covariance matrix
`X'y`. It is able to project both the training data `X` and the targets
`y`. The training data `X` is projected on the left ... | 6 | 4 | 29 | 3 | 15 | 11 | 2 | 1.48 | 3 | 1 | 0 | 0 | 4 | 10 | 4 | 40 | 195 | 31 | 66 | 23 | 60 | 98 | 42 | 22 | 37 | 3 | 2 | 2 | 8 |
322,493 | 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/cross_decomposition/_pls.py | sklearn.cross_decomposition._pls._PLS | from abc import ABCMeta, abstractmethod
from ..utils import check_array, check_consistent_length
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, MultiOutputMixin, RegressorMixin, TransformerMixin, _fit_context
from ..utils._param_validation import Interval, StrOptions
from numbers import Integral, Re... |
class _PLS(ClassNamePrefixFeaturesOutMixin, TransformerMixin, RegressorMixin, MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
'''Partial Least Squares (PLS)
This class implements the generic PLS algorithm.
Main ref: Wegelin, a survey of Partial Least Squares (PLS) methods,
with emphasis on the two... | 10 | 6 | 45 | 5 | 24 | 16 | 3 | 0.63 | 6 | 6 | 0 | 3 | 7 | 26 | 7 | 66 | 350 | 48 | 190 | 76 | 162 | 119 | 118 | 53 | 110 | 12 | 3 | 4 | 22 |
322,494 | 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/datasets/_openml.py | sklearn.datasets._openml.OpenMLError | class OpenMLError(ValueError):
"""HTTP 412 is a specific OpenML error code, indicating a generic error"""
pass | class OpenMLError(ValueError):
'''HTTP 412 is a specific OpenML error code, indicating a generic error'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 4 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 0 | 4 | 0 | 0 |
322,495 | 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/decomposition/_base.py | sklearn.decomposition._base._BasePCA | from abc import ABCMeta, abstractmethod
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin
from ..utils.validation import check_is_fitted, validate_data
import numpy as np
from scipy import linalg
from ..utils._array_api import _fill_or_add_to_diagonal, device, get_namespace
class _Bas... |
class _BasePCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator, metaclass=ABCMeta):
'''Base class for PCA methods.
Warning: This class should not be used directly.
Use derived classes instead.
'''
def get_covariance(self):
'''Compute data covariance with the generative... | 10 | 7 | 24 | 3 | 11 | 10 | 2 | 0.95 | 4 | 0 | 0 | 2 | 7 | 1 | 7 | 63 | 187 | 31 | 80 | 31 | 68 | 76 | 58 | 26 | 50 | 5 | 3 | 1 | 15 |
322,496 | 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/decomposition/_dict_learning.py | sklearn.decomposition._dict_learning.DictionaryLearning | from ..utils._param_validation import Interval, StrOptions, validate_params
import numpy as np
from ..utils.validation import check_is_fitted, validate_data
from ..utils import check_array, check_random_state, gen_batches, gen_even_slices
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMix... |
class DictionaryLearning(_BaseSparseCoding, BaseEstimator):
'''Dictionary learning.
Finds a dictionary (a set of atoms) that performs well at sparsely
encoding the fitted data.
Solves the optimization problem::
(U^*,V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1
... | 8 | 4 | 24 | 2 | 16 | 5 | 1 | 1.62 | 2 | 1 | 0 | 0 | 5 | 16 | 5 | 46 | 341 | 63 | 106 | 50 | 77 | 172 | 36 | 25 | 30 | 2 | 3 | 1 | 6 |
322,497 | 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/decomposition/_dict_learning.py | sklearn.decomposition._dict_learning.MiniBatchDictionaryLearning | from ..utils._param_validation import Interval, StrOptions, validate_params
from numbers import Integral, Real
from ..utils.validation import check_is_fitted, validate_data
import itertools
from ..utils.extmath import _randomized_svd, row_norms, svd_flip
from scipy import linalg
from ..utils import check_array, check_r... |
class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
'''Mini-batch dictionary learning.
Finds a dictionary (a set of atoms) that performs well at sparsely
encoding the fitted data.
Solves the optimization problem::
(U^*,V^*) = argmin 0.5 || X - U V ||_Fro^2 + alpha * || U ||_1,1
... | 14 | 8 | 33 | 5 | 22 | 6 | 3 | 0.85 | 2 | 5 | 0 | 0 | 10 | 29 | 10 | 51 | 579 | 115 | 252 | 93 | 213 | 213 | 136 | 60 | 125 | 10 | 3 | 2 | 29 |
322,498 | 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/decomposition/_dict_learning.py | sklearn.decomposition._dict_learning.SparseCoder | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
class SparseCoder(_BaseSparseCoding, BaseEstimator):
"""Sparse coding.
Finds a sparse representation of data against a fixed, precomputed
dictionary.
Each row of the result is the solution to a sparse co... |
class SparseCoder(_BaseSparseCoding, BaseEstimator):
'''Sparse coding.
Finds a sparse representation of data against a fixed, precomputed
dictionary.
Each row of the result is the solution to a sparse coding problem.
The goal is to find a sparse array `code` such that::
X ~= code * dictiona... | 12 | 7 | 12 | 1 | 5 | 5 | 1 | 3.23 | 2 | 1 | 0 | 0 | 8 | 1 | 8 | 49 | 222 | 40 | 43 | 25 | 20 | 139 | 21 | 11 | 12 | 1 | 3 | 0 | 8 |
322,499 | 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/decomposition/_dict_learning.py | sklearn.decomposition._dict_learning._BaseSparseCoding | from ..utils import check_array, check_random_state, gen_batches, gen_even_slices
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
import numpy as np
from ..utils.validation import check_is_fitted, validate_data
class _BaseSparseCoding(ClassNamePrefixFeaturesOutMixin, T... |
class _BaseSparseCoding(ClassNamePrefixFeaturesOutMixin, TransformerMixin):
'''Base class from SparseCoder and DictionaryLearning algorithms.'''
def __init__(self, transform_algorithm, transform_n_nonzero_coefs, transform_alpha, split_sign, n_jobs, positive_code, transform_max_iter):
pass
def _tr... | 6 | 5 | 20 | 2 | 12 | 6 | 2 | 0.49 | 2 | 1 | 0 | 3 | 5 | 7 | 5 | 10 | 109 | 15 | 63 | 28 | 48 | 31 | 40 | 19 | 34 | 4 | 2 | 1 | 10 |
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