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322,800 | 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/multioutput.py | sklearn.multioutput.MultiOutputClassifier | from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, is_classifier
from .utils.validation import _check_method_params, _check_response_method, check_is_fitted, has_fit_parameter, validate_data
import numpy as np
from .utils.metaestimators import available_if
class ... |
class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):
'''Multi target classification.
This strategy consists of fitting one classifier per target. This is a
simple strategy for extending classifiers that do not natively support
multi-target classification.
Parameters
----------
... | 8 | 4 | 17 | 2 | 6 | 9 | 2 | 2.89 | 2 | 2 | 0 | 0 | 6 | 1 | 6 | 65 | 177 | 33 | 37 | 13 | 29 | 107 | 30 | 12 | 23 | 3 | 4 | 1 | 9 |
322,801 | 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/multioutput.py | sklearn.multioutput.MultiOutputRegressor | from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, is_classifier
class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
"""Multi target regression.
This strategy consists of fitting one regressor per target. This is a
simple strategy for ... |
class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
'''Multi target regression.
This strategy consists of fitting one regressor per target. This is a
simple strategy for extending regressors that do not natively support
multi-target regression.
.. versionadded:: 0.18
Parameters
... | 4 | 2 | 17 | 4 | 2 | 11 | 1 | 12 | 2 | 1 | 0 | 0 | 2 | 0 | 2 | 61 | 101 | 23 | 6 | 4 | 2 | 72 | 5 | 3 | 2 | 1 | 4 | 0 | 2 |
322,802 | 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/multioutput.py | sklearn.multioutput.RegressorChain | from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, is_classifier
from .utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
"""A... |
class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
'''A multi-label model that arranges regressions into a chain.
Each model makes a prediction in the order specified by the chain using
all of the available features provided to the model plus the predictions
of models that are earlie... | 5 | 3 | 16 | 3 | 5 | 9 | 1 | 5.72 | 3 | 3 | 2 | 0 | 3 | 0 | 3 | 63 | 158 | 37 | 18 | 9 | 11 | 103 | 12 | 6 | 8 | 1 | 4 | 0 | 3 |
322,803 | 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/multioutput.py | sklearn.multioutput._BaseChain | from .utils.validation import _check_method_params, _check_response_method, check_is_fitted, has_fit_parameter, validate_data
from abc import ABCMeta, abstractmethod
from .model_selection import cross_val_predict
from .utils._param_validation import HasMethods, Hidden, StrOptions
import numpy as np
from .utils import B... |
class _BaseChain(BaseEstimator, metaclass=ABCMeta):
def __init__(self, estimator=None, *, order=None, cv=None, random_state=None, verbose=False, base_estimator='deprecated'):
pass
def _get_estimator(self):
'''Get and validate estimator.'''
pass
def _log_message(self, *, estimator... | 9 | 4 | 30 | 4 | 21 | 6 | 4 | 0.28 | 2 | 11 | 3 | 2 | 7 | 9 | 7 | 58 | 237 | 33 | 160 | 51 | 142 | 44 | 95 | 41 | 87 | 17 | 3 | 3 | 29 |
322,804 | 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/multioutput.py | sklearn.multioutput._MultiOutputEstimator | from .utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
from .utils.validation import _check_method_params, _check_response_method, check_is_fitted, has_fit_parameter, validate_data
from .utils._param_validation import HasMethods, Hidden, StrOptions
from .... |
class _MultiOutputEstimator(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
@abstractmethod
def __init__(self, estimator, *, n_jobs=None):
pass
@_available_if_estimator_has('partial_fit')
@_fit_context(prefer_skip_nested_validation=False)
def partial_fit(self, X, y, classes=None, sa... | 11 | 4 | 37 | 6 | 19 | 12 | 4 | 0.58 | 3 | 7 | 4 | 2 | 6 | 5 | 6 | 57 | 241 | 43 | 125 | 25 | 110 | 73 | 65 | 20 | 58 | 10 | 3 | 2 | 25 |
322,805 | 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/naive_bayes.py | sklearn.naive_bayes.BernoulliNB | from numbers import Integral, Real
import numpy as np
from .utils.extmath import safe_sparse_dot
from .preprocessing import LabelBinarizer, binarize, label_binarize
from .utils._param_validation import Interval
class BernoulliNB(_BaseDiscreteNB):
"""Naive Bayes classifier for multivariate Bernoulli models.
Li... |
class BernoulliNB(_BaseDiscreteNB):
'''Naive Bayes classifier for multivariate Bernoulli models.
Like MultinomialNB, this classifier is suitable for discrete data. The
difference is that while MultinomialNB works with occurrence counts,
BernoulliNB is designed for binary/boolean features.
Read more... | 7 | 5 | 9 | 1 | 8 | 1 | 2 | 1.6 | 1 | 2 | 0 | 0 | 6 | 2 | 6 | 76 | 168 | 33 | 52 | 24 | 37 | 83 | 31 | 16 | 24 | 2 | 5 | 1 | 9 |
322,806 | 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/naive_bayes.py | sklearn.naive_bayes.CategoricalNB | from numbers import Integral, Real
import numpy as np
from .utils.validation import _check_n_features, _check_sample_weight, check_is_fitted, check_non_negative, validate_data
from .utils._param_validation import Interval
class CategoricalNB(_BaseDiscreteNB):
"""Naive Bayes classifier for categorical features.
... |
class CategoricalNB(_BaseDiscreteNB):
'''Naive Bayes classifier for categorical features.
The categorical Naive Bayes classifier is suitable for classification with
discrete features that are categorically distributed. The categories of
each feature are drawn from a categorical distribution.
Read m... | 15 | 4 | 15 | 1 | 10 | 4 | 2 | 1.07 | 1 | 4 | 0 | 0 | 10 | 5 | 11 | 81 | 309 | 50 | 125 | 49 | 102 | 134 | 73 | 40 | 59 | 4 | 5 | 2 | 22 |
322,807 | 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/naive_bayes.py | sklearn.naive_bayes.ComplementNB | from .utils.extmath import safe_sparse_dot
from .utils.validation import _check_n_features, _check_sample_weight, check_is_fitted, check_non_negative, validate_data
import numpy as np
class ComplementNB(_BaseDiscreteNB):
"""The Complement Naive Bayes classifier described in Rennie et al. (2003).
The Complemen... |
class ComplementNB(_BaseDiscreteNB):
'''The Complement Naive Bayes classifier described in Rennie et al. (2003).
The Complement Naive Bayes classifier was designed to correct the "severe
assumptions" made by the standard Multinomial Naive Bayes classifier. It is
particularly suited for imbalanced data ... | 6 | 4 | 9 | 0 | 8 | 1 | 1 | 1.84 | 1 | 1 | 0 | 0 | 5 | 3 | 5 | 75 | 153 | 28 | 44 | 24 | 30 | 81 | 27 | 16 | 21 | 2 | 5 | 1 | 7 |
322,808 | 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/naive_bayes.py | sklearn.naive_bayes.GaussianNB | from .utils._param_validation import Interval
from numbers import Integral, Real
from .base import BaseEstimator, ClassifierMixin, _fit_context
from .utils.validation import _check_n_features, _check_sample_weight, check_is_fitted, check_non_negative, validate_data
import numpy as np
from .utils.multiclass import _chec... |
class GaussianNB(_BaseNB):
'''
Gaussian Naive Bayes (GaussianNB).
Can perform online updates to model parameters via :meth:`partial_fit`.
For details on algorithm used to update feature means and variance online,
see `Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque
<http://i.... | 11 | 6 | 39 | 8 | 15 | 17 | 3 | 1.62 | 1 | 3 | 0 | 0 | 6 | 8 | 7 | 66 | 368 | 80 | 110 | 46 | 99 | 178 | 91 | 43 | 83 | 13 | 4 | 3 | 24 |
322,809 | 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/naive_bayes.py | sklearn.naive_bayes.MultinomialNB | from .utils.validation import _check_n_features, _check_sample_weight, check_is_fitted, check_non_negative, validate_data
import numpy as np
from .utils.extmath import safe_sparse_dot
class MultinomialNB(_BaseDiscreteNB):
"""
Naive Bayes classifier for multinomial models.
The multinomial Naive Bayes class... |
class MultinomialNB(_BaseDiscreteNB):
'''
Naive Bayes classifier for multinomial models.
The multinomial Naive Bayes classifier is suitable for classification with
discrete features (e.g., word counts for text classification). The
multinomial distribution normally requires integer feature counts. H... | 6 | 4 | 6 | 0 | 5 | 1 | 1 | 2.88 | 1 | 1 | 0 | 0 | 5 | 1 | 5 | 75 | 126 | 25 | 26 | 12 | 18 | 75 | 17 | 10 | 11 | 1 | 5 | 0 | 5 |
322,810 | 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/naive_bayes.py | sklearn.naive_bayes._BaseDiscreteNB | from numbers import Integral, Real
from .preprocessing import LabelBinarizer, binarize, label_binarize
import numpy as np
from .utils.multiclass import _check_partial_fit_first_call
from .utils.validation import _check_n_features, _check_sample_weight, check_is_fitted, check_non_negative, validate_data
from .utils._par... |
class _BaseDiscreteNB(_BaseNB):
'''Abstract base class for naive Bayes on discrete/categorical data
Any estimator based on this class should provide:
__init__
_joint_log_likelihood(X) as per _BaseNB
_update_feature_log_prob(alpha)
_count(X, Y)
'''
def __init__(self, alpha=1.0, fit_prio... | 16 | 8 | 20 | 3 | 10 | 8 | 2 | 0.84 | 1 | 6 | 1 | 4 | 11 | 8 | 11 | 70 | 256 | 45 | 116 | 44 | 100 | 97 | 94 | 40 | 82 | 6 | 4 | 2 | 27 |
322,811 | 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/naive_bayes.py | sklearn.naive_bayes._BaseNB | from scipy.special import logsumexp
from .utils.validation import _check_n_features, _check_sample_weight, check_is_fitted, check_non_negative, validate_data
import numpy as np
from abc import ABCMeta, abstractmethod
from .base import BaseEstimator, ClassifierMixin, _fit_context
class _BaseNB(ClassifierMixin, BaseEsti... |
class _BaseNB(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):
'''Abstract base class for naive Bayes estimators'''
@abstractmethod
def _joint_log_likelihood(self, X):
'''Compute the unnormalized posterior log probability of X
I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an arr... | 9 | 7 | 16 | 2 | 3 | 11 | 1 | 3 | 3 | 0 | 0 | 2 | 6 | 0 | 6 | 59 | 106 | 18 | 22 | 12 | 13 | 66 | 20 | 10 | 13 | 1 | 3 | 0 | 6 |
322,812 | 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/neighbors/_base.py | sklearn.neighbors._base.KNeighborsMixin | from ..metrics._pairwise_distances_reduction import ArgKmin, RadiusNeighbors
from ..utils.validation import _to_object_array, check_is_fitted, validate_data
from functools import partial
from scipy.sparse import csr_matrix, issparse
from ..metrics import DistanceMetric, pairwise_distances_chunked
from ..utils.parallel ... |
class KNeighborsMixin:
'''Mixin for k-neighbors searches.'''
def _kneighbors_reduce_func(self, dist, start, n_neighbors, return_distance):
'''Reduce a chunk of distances to the nearest neighbors.
Callback to :func:`sklearn.metrics.pairwise.pairwise_distances_chunked`
Parameters
... | 4 | 4 | 108 | 15 | 53 | 41 | 9 | 0.79 | 0 | 10 | 3 | 5 | 3 | 4 | 3 | 3 | 330 | 48 | 159 | 33 | 155 | 125 | 85 | 29 | 81 | 21 | 0 | 2 | 28 |
322,813 | 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/neighbors/_base.py | sklearn.neighbors._base.NeighborsBase | from ..utils.validation import _to_object_array, check_is_fitted, validate_data
from ..utils._param_validation import Interval, StrOptions, validate_params
from scipy.sparse import csr_matrix, issparse
from ..metrics import DistanceMetric, pairwise_distances_chunked
from abc import ABCMeta, abstractmethod
from ._kd_tre... |
class NeighborsBase(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
'''Base class for nearest neighbors estimators.'''
@abstractmethod
def __init__(self, n_neighbors=None, radius=None, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=None):
pass
def _che... | 6 | 1 | 76 | 6 | 66 | 5 | 12 | 0.08 | 3 | 5 | 1 | 8 | 4 | 18 | 4 | 56 | 323 | 28 | 274 | 44 | 258 | 22 | 130 | 33 | 125 | 37 | 3 | 4 | 48 |
322,814 | 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/neighbors/_base.py | sklearn.neighbors._base.RadiusNeighborsMixin | from ..metrics._pairwise_distances_reduction import ArgKmin, RadiusNeighbors
import itertools
import numpy as np
from ..utils import check_array, gen_even_slices, get_tags
from joblib import effective_n_jobs
from ..utils.parallel import Parallel, delayed
from ..metrics import DistanceMetric, pairwise_distances_chunked
... |
class RadiusNeighborsMixin:
'''Mixin for radius-based neighbors searches.'''
def _radius_neighbors_reduce_func(self, dist, start, radius, return_distance):
'''Reduce a chunk of distances to the nearest neighbors.
Callback to :func:`sklearn.metrics.pairwise.pairwise_distances_chunked`
P... | 5 | 4 | 87 | 13 | 40 | 34 | 8 | 0.87 | 0 | 13 | 3 | 4 | 4 | 4 | 4 | 4 | 354 | 57 | 159 | 41 | 150 | 138 | 93 | 33 | 88 | 22 | 0 | 3 | 31 |
322,815 | 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/neighbors/_classification.py | sklearn.neighbors._classification.KNeighborsClassifier | from ..utils.validation import _is_arraylike, _num_samples, check_is_fitted, validate_data
from ..utils._param_validation import StrOptions
from ..metrics._pairwise_distances_reduction import ArgKminClassMode, RadiusNeighborsClassMode
from sklearn.neighbors._base import _check_precomputed
from ..utils.extmath import we... |
class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase):
'''Classifier implementing the k-nearest neighbors vote.
Read more in the :ref:`User Guide <classification>`.
Parameters
----------
n_neighbors : int, default=5
Number of neighbors to use by default for :meth:`knei... | 8 | 5 | 41 | 5 | 23 | 14 | 4 | 1.38 | 3 | 4 | 1 | 1 | 6 | 7 | 6 | 67 | 413 | 66 | 146 | 54 | 125 | 202 | 83 | 35 | 76 | 11 | 4 | 3 | 25 |
322,816 | 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/neighbors/_classification.py | sklearn.neighbors._classification.RadiusNeighborsClassifier | import numpy as np
from ..utils.validation import _is_arraylike, _num_samples, check_is_fitted, validate_data
from numbers import Integral
from ..metrics._pairwise_distances_reduction import ArgKminClassMode, RadiusNeighborsClassMode
from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin, _get_weights
... |
class RadiusNeighborsClassifier(RadiusNeighborsMixin, ClassifierMixin, NeighborsBase):
'''Classifier implementing a vote among neighbors within a given radius.
Read more in the :ref:`User Guide <classification>`.
Parameters
----------
radius : float, default=1.0
Range of parameter space to ... | 8 | 5 | 48 | 7 | 28 | 13 | 5 | 1.14 | 3 | 8 | 1 | 0 | 6 | 9 | 6 | 68 | 465 | 79 | 180 | 65 | 158 | 206 | 104 | 45 | 97 | 13 | 4 | 3 | 31 |
322,817 | 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/neighbors/_graph.py | sklearn.neighbors._graph.KNeighborsTransformer | from ._base import VALID_METRICS, KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin
from ..utils.validation import check_is_fitted
from ..utils._param_validation import Integral, Interval, Real, StrOptions, validate_params
from ..base import ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
class KNei... |
class KNeighborsTransformer(ClassNamePrefixFeaturesOutMixin, KNeighborsMixin, TransformerMixin, NeighborsBase):
'''Transform X into a (weighted) graph of k nearest neighbors.
The transformed data is a sparse graph as returned by kneighbors_graph.
Read more in the :ref:`User Guide <neighbors_transformer>`.
... | 6 | 4 | 22 | 2 | 9 | 11 | 1 | 3.04 | 4 | 1 | 0 | 0 | 4 | 2 | 4 | 68 | 224 | 38 | 46 | 25 | 25 | 140 | 16 | 9 | 11 | 1 | 4 | 0 | 4 |
322,818 | 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/neighbors/_graph.py | sklearn.neighbors._graph.RadiusNeighborsTransformer | from ..utils.validation import check_is_fitted
from ._base import VALID_METRICS, KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin
from ..utils._param_validation import Integral, Interval, Real, StrOptions, validate_params
from ..base import ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
class Radi... |
class RadiusNeighborsTransformer(ClassNamePrefixFeaturesOutMixin, RadiusNeighborsMixin, TransformerMixin, NeighborsBase):
'''Transform X into a (weighted) graph of neighbors nearer than a radius.
The transformed data is a sparse graph as returned by
`radius_neighbors_graph`.
Read more in the :ref:`User... | 6 | 4 | 21 | 2 | 8 | 11 | 1 | 2.98 | 4 | 1 | 0 | 0 | 4 | 2 | 4 | 69 | 221 | 38 | 46 | 27 | 22 | 137 | 15 | 8 | 10 | 1 | 4 | 0 | 4 |
322,819 | 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/neighbors/_kde.py | sklearn.neighbors._kde.KernelDensity | import itertools
from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data
from ._kd_tree import KDTree
from ..utils._param_validation import Interval, StrOptions
from ._ball_tree import BallTree
from ..base import BaseEstimator, _fit_context
from ..utils.extmath import row_norms
from numbers ... |
class KernelDensity(BaseEstimator):
'''Kernel Density Estimation.
Read more in the :ref:`User Guide <kernel_density>`.
Parameters
----------
bandwidth : float or {"scott", "silverman"}, default=1.0
The bandwidth of the kernel. If bandwidth is a float, it defines the
bandwidth of the... | 8 | 5 | 33 | 4 | 18 | 12 | 3 | 1.13 | 1 | 3 | 0 | 0 | 6 | 11 | 6 | 37 | 322 | 48 | 129 | 49 | 107 | 146 | 66 | 34 | 59 | 6 | 2 | 2 | 20 |
322,820 | 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/neighbors/_lof.py | sklearn.neighbors._lof.LocalOutlierFactor | from ..utils import check_array
from ..base import OutlierMixin, _fit_context
from ._base import KNeighborsMixin, NeighborsBase
import warnings
from ..utils.metaestimators import available_if
import numpy as np
from ..utils._param_validation import Interval, StrOptions
from numbers import Real
from ..utils.validation i... |
class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):
'''Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
The anomaly score of each sample is called the Local Outlier Factor.
It measures the local deviation of the density of a given sample with respect
to its neigh... | 18 | 8 | 25 | 4 | 11 | 10 | 2 | 1.66 | 3 | 3 | 0 | 0 | 12 | 8 | 12 | 73 | 500 | 90 | 154 | 54 | 122 | 256 | 73 | 34 | 60 | 5 | 4 | 1 | 22 |
322,821 | 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/neighbors/_nca.py | sklearn.neighbors._nca.NeighborhoodComponentsAnalysis | from ..exceptions import ConvergenceWarning
from numbers import Integral, Real
from scipy.optimize import minimize
from ..decomposition import PCA
import sys
from ..preprocessing import LabelEncoder
from ..utils._param_validation import Interval, StrOptions
from ..utils.fixes import _get_additional_lbfgs_options_dict
f... |
class NeighborhoodComponentsAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Neighborhood Components Analysis.
Neighborhood Component Analysis (NCA) is a machine learning algorithm for
metric learning. It learns a linear transformation in a supervised fashion
to improve th... | 11 | 7 | 40 | 5 | 24 | 11 | 4 | 0.99 | 3 | 7 | 4 | 0 | 8 | 11 | 8 | 44 | 501 | 78 | 214 | 67 | 189 | 212 | 121 | 52 | 111 | 14 | 2 | 4 | 34 |
322,822 | 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/neighbors/_nearest_centroid.py | sklearn.neighbors._nearest_centroid.NearestCentroid | from ..base import BaseEstimator, ClassifierMixin, _fit_context
from scipy import sparse as sp
from ..metrics.pairwise import pairwise_distances, pairwise_distances_argmin
from ..discriminant_analysis import DiscriminantAnalysisPredictionMixin
from ..utils import get_tags
from numbers import Real
from ..preprocessing i... |
class NearestCentroid(DiscriminantAnalysisPredictionMixin, ClassifierMixin, BaseEstimator):
'''Nearest centroid classifier.
Each class is represented by its centroid, with test samples classified to
the class with the nearest centroid.
Read more in the :ref:`User Guide <nearest_centroid_classifier>`.
... | 8 | 3 | 34 | 3 | 24 | 7 | 4 | 0.75 | 3 | 6 | 1 | 0 | 6 | 8 | 6 | 43 | 331 | 56 | 159 | 55 | 143 | 120 | 94 | 46 | 87 | 16 | 2 | 3 | 24 |
322,823 | 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/neighbors/_regression.py | sklearn.neighbors._regression.KNeighborsRegressor | from ..base import RegressorMixin, _fit_context
from ..metrics import DistanceMetric
from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin, _get_weights
from ..utils._param_validation import StrOptions
import numpy as np
class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):
"... |
class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):
'''Regression based on k-nearest neighbors.
The target is predicted by local interpolation of the targets
associated of the nearest neighbors in the training set.
Read more in the :ref:`User Guide <regression>`.
.. versionad... | 6 | 3 | 23 | 3 | 12 | 8 | 2 | 2.46 | 3 | 2 | 0 | 0 | 4 | 1 | 4 | 65 | 252 | 48 | 59 | 30 | 40 | 145 | 32 | 16 | 27 | 6 | 4 | 2 | 9 |
322,824 | 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/neighbors/_regression.py | sklearn.neighbors._regression.RadiusNeighborsRegressor | import numpy as np
from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin, _get_weights
from ..utils._param_validation import StrOptions
from ..base import RegressorMixin, _fit_context
import warnings
class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBase):
"""Regressio... |
class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBase):
'''Regression based on neighbors within a fixed radius.
The target is predicted by local interpolation of the targets
associated of the nearest neighbors in the training set.
Read more in the :ref:`User Guide <regressi... | 5 | 3 | 33 | 4 | 19 | 9 | 3 | 1.97 | 3 | 2 | 0 | 0 | 3 | 1 | 3 | 65 | 244 | 45 | 67 | 27 | 49 | 132 | 24 | 12 | 20 | 7 | 4 | 1 | 9 |
322,825 | 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/neighbors/_unsupervised.py | sklearn.neighbors._unsupervised.NearestNeighbors | from ..base import _fit_context
from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin
class NearestNeighbors(KNeighborsMixin, RadiusNeighborsMixin, NeighborsBase):
"""Unsupervised learner for implementing neighbor searches.
Read more in the :ref:`User Guide <unsupervised_neighbors>`.
..... |
class NearestNeighbors(KNeighborsMixin, RadiusNeighborsMixin, NeighborsBase):
'''Unsupervised learner for implementing neighbor searches.
Read more in the :ref:`User Guide <unsupervised_neighbors>`.
.. versionadded:: 0.9
Parameters
----------
n_neighbors : int, default=5
Number of neigh... | 4 | 2 | 20 | 2 | 12 | 7 | 1 | 4 | 3 | 1 | 0 | 0 | 2 | 0 | 2 | 65 | 170 | 30 | 28 | 17 | 11 | 112 | 5 | 3 | 2 | 1 | 4 | 0 | 2 |
322,826 | 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/neural_network/_multilayer_perceptron.py | sklearn.neural_network._multilayer_perceptron.BaseMultilayerPerceptron | from itertools import chain, pairwise
from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
import scipy.optimize
from abc import ABC, abstractmethod
from ..utils.fixes import _get_additional_lbfgs_options_dict
from ..e... | null | 20 | 9 | 45 | 6 | 29 | 10 | 5 | 0.32 | 2 | 11 | 3 | 2 | 17 | 43 | 17 | 68 | 819 | 115 | 533 | 191 | 452 | 171 | 291 | 128 | 273 | 25 | 4 | 4 | 78 |
322,827 | 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/neural_network/_multilayer_perceptron.py | sklearn.neural_network._multilayer_perceptron.MLPClassifier | import numpy as np
from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
from ..utils import _safe_indexing, check_random_state, column_or_1d, gen_batches, shuffle
from ..preprocessing import LabelBinarizer
from ..metrics import accuracy_score, r2_score
from ..utils.multiclass i... |
class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):
'''Multi-layer Perceptron classifier.
This model optimizes the log-loss function using LBFGS or stochastic
gradient descent.
.. versionadded:: 0.18
Parameters
----------
hidden_layer_sizes : array-like of shape(n_layers - 2,), ... | 12 | 6 | 24 | 2 | 13 | 8 | 2 | 2.31 | 2 | 5 | 1 | 0 | 9 | 2 | 9 | 79 | 505 | 95 | 124 | 44 | 86 | 286 | 50 | 17 | 40 | 6 | 5 | 3 | 19 |
322,828 | 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/neural_network/_multilayer_perceptron.py | sklearn.neural_network._multilayer_perceptron.MLPRegressor | from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
from ..utils.metaestimators import available_if
from ..metrics import accuracy_score, r2_score
from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data
import numpy as np
from ..utils import _safe_i... |
class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
'''Multi-layer Perceptron regressor.
This model optimizes the squared error using LBFGS or stochastic gradient
descent.
.. versionadded:: 0.18
Parameters
----------
loss : {'squared_error', 'poisson'}, default='squared_error'
... | 9 | 4 | 19 | 1 | 14 | 4 | 1 | 2.76 | 2 | 1 | 0 | 0 | 6 | 0 | 6 | 76 | 416 | 81 | 89 | 37 | 53 | 246 | 21 | 9 | 14 | 2 | 5 | 1 | 8 |
322,829 | 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/neural_network/_rbm.py | sklearn.neural_network._rbm.BernoulliRBM | from scipy.special import expit
import scipy.sparse as sp
from ..utils._param_validation import Interval
import numpy as np
from ..utils.validation import check_is_fitted, validate_data
from ..utils import check_random_state, gen_even_slices
import time
from ..utils.extmath import safe_sparse_dot
from ..base import Bas... |
class BernoulliRBM(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Bernoulli Restricted Boltzmann Machine (RBM).
A Restricted Boltzmann Machine with binary visible units and
binary hidden units. Parameters are estimated using Stochastic Maximum
Likelihood (SML), also known as Pers... | 15 | 11 | 24 | 3 | 12 | 10 | 2 | 1.28 | 3 | 9 | 2 | 0 | 12 | 12 | 12 | 48 | 421 | 79 | 151 | 65 | 127 | 194 | 102 | 52 | 89 | 6 | 2 | 2 | 23 |
322,830 | 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/neural_network/_stochastic_optimizers.py | sklearn.neural_network._stochastic_optimizers.AdamOptimizer | import numpy as np
class AdamOptimizer(BaseOptimizer):
"""Stochastic gradient descent optimizer with Adam
Note: All default values are from the original Adam paper
Parameters
----------
params : list, length = len(coefs_) + len(intercepts_)
The concatenated list containing coefs_ and inte... |
class AdamOptimizer(BaseOptimizer):
'''Stochastic gradient descent optimizer with Adam
Note: All default values are from the original Adam paper
Parameters
----------
params : list, length = len(coefs_) + len(intercepts_)
The concatenated list containing coefs_ and intercepts_ in MLP model.... | 3 | 2 | 22 | 2 | 15 | 6 | 1 | 1.42 | 1 | 2 | 0 | 0 | 2 | 7 | 2 | 6 | 91 | 16 | 31 | 13 | 26 | 44 | 16 | 11 | 13 | 1 | 1 | 0 | 2 |
322,831 | 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/neural_network/_stochastic_optimizers.py | sklearn.neural_network._stochastic_optimizers.BaseOptimizer | class BaseOptimizer:
"""Base (Stochastic) gradient descent optimizer
Parameters
----------
learning_rate_init : float, default=0.1
The initial learning rate used. It controls the step-size in updating
the weights
Attributes
----------
learning_rate : float
the curre... | class BaseOptimizer:
'''Base (Stochastic) gradient descent optimizer
Parameters
----------
learning_rate_init : float, default=0.1
The initial learning rate used. It controls the step-size in updating
the weights
Attributes
----------
learning_rate : float
the current... | 5 | 4 | 11 | 1 | 3 | 6 | 2 | 2.57 | 0 | 2 | 0 | 2 | 4 | 2 | 4 | 4 | 61 | 11 | 14 | 9 | 9 | 36 | 14 | 9 | 9 | 2 | 0 | 1 | 6 |
322,832 | 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/neural_network/_stochastic_optimizers.py | sklearn.neural_network._stochastic_optimizers.SGDOptimizer | import numpy as np
class SGDOptimizer(BaseOptimizer):
"""Stochastic gradient descent optimizer with momentum
Parameters
----------
params : list, length = len(coefs_) + len(intercepts_)
The concatenated list containing coefs_ and intercepts_ in MLP model.
Used for initializing velociti... |
class SGDOptimizer(BaseOptimizer):
'''Stochastic gradient descent optimizer with momentum
Parameters
----------
params : list, length = len(coefs_) + len(intercepts_)
The concatenated list containing coefs_ and intercepts_ in MLP model.
Used for initializing velocities and updating para... | 5 | 3 | 18 | 2 | 11 | 5 | 3 | 1.17 | 1 | 3 | 0 | 0 | 4 | 6 | 4 | 8 | 123 | 23 | 46 | 20 | 33 | 54 | 30 | 12 | 25 | 6 | 1 | 2 | 11 |
322,833 | 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/pipeline.py | sklearn.pipeline.FeatureUnion | from .utils._repr_html.estimator import _VisualBlock
from itertools import chain, islice
from .utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, get_routing_for_object, process_routing
from .utils.validation import check_is_fitted, check_memory
from .utils.parallel import... | null | 28 | 15 | 18 | 2 | 9 | 7 | 2 | 1.1 | 2 | 20 | 6 | 0 | 24 | 5 | 24 | 84 | 571 | 102 | 224 | 74 | 188 | 246 | 151 | 60 | 126 | 5 | 4 | 3 | 57 |
322,834 | 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/pipeline.py | sklearn.pipeline.Pipeline | from .utils._user_interface import _print_elapsed_time
from .base import TransformerMixin, _fit_context, clone
from .utils.metaestimators import _BaseComposition, available_if
from .utils._metadata_requests import METHODS
from .utils._tags import get_tags
from .utils import Bunch
from .utils._set_output import _get_con... |
class Pipeline(_BaseComposition):
'''
A sequence of data transformers with an optional final predictor.
`Pipeline` allows you to sequentially apply a list of transformers to
preprocess the data and, if desired, conclude the sequence with a final
:term:`predictor` for predictive modeling.
Interm... | 58 | 28 | 29 | 4 | 12 | 13 | 3 | 1.22 | 1 | 17 | 5 | 2 | 37 | 4 | 37 | 93 | 1,285 | 209 | 485 | 141 | 421 | 593 | 298 | 119 | 259 | 6 | 4 | 3 | 102 |
322,835 | 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/preprocessing/_data.py | sklearn.preprocessing._data.Binarizer | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_data
from numbers import Integral, Real
class Binarizer(OneToOneFeatureMixin, Transfor... |
class Binarizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Binarize data (set feature values to 0 or 1) according to a threshold.
Values greater than the threshold map to 1, while values less than
or equal to the threshold map to 0. With the default threshold of 0,
only positive values ... | 6 | 3 | 15 | 2 | 6 | 8 | 1 | 2.93 | 3 | 1 | 0 | 0 | 4 | 2 | 4 | 40 | 140 | 26 | 29 | 10 | 23 | 85 | 18 | 9 | 13 | 2 | 2 | 0 | 5 |
322,836 | 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/preprocessing/_data.py | sklearn.preprocessing._data.KernelCenterer | from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_data
from ..utils import _array_api, check_array, resample
from sklearn.utils import metadata_routing
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin... |
class KernelCenterer(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
'''Center an arbitrary kernel matrix :math:`K`.
Let define a kernel :math:`K` such that:
.. math::
K(X, Y) = \phi(X) . \phi(Y)^{T}
:math:`\phi(X)` is a function mapping of rows of :math:`X` to a
Hilbert ... | 6 | 4 | 20 | 4 | 9 | 7 | 1 | 2.28 | 3 | 2 | 0 | 0 | 4 | 2 | 4 | 40 | 164 | 36 | 39 | 15 | 33 | 89 | 28 | 14 | 23 | 2 | 2 | 1 | 5 |
322,837 | 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/preprocessing/_data.py | sklearn.preprocessing._data.MaxAbsScaler | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils._array_api import _find_matching_floating_dtype, _modify_in_place_if_numpy, device, get_namespace, get_namespace_and_device
import numpy as np
from scipy import sparse, stats
from ..utils... |
class MaxAbsScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Scale each feature by its maximum absolute value.
This estimator scales and translates each feature individually such
that the maximal absolute value of each feature in the
training set will be 1.0. It does not shift/center t... | 9 | 6 | 21 | 3 | 10 | 8 | 2 | 1.43 | 3 | 1 | 0 | 0 | 7 | 4 | 7 | 43 | 231 | 46 | 76 | 21 | 67 | 109 | 48 | 20 | 40 | 3 | 2 | 1 | 12 |
322,838 | 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/preprocessing/_data.py | sklearn.preprocessing._data.MinMaxScaler | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils import _array_api, check_array, resample
from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_data
from scipy import sparse, st... |
class MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Transform features by scaling each feature to a given range.
This estimator scales and translates each feature individually such
that it is in the given range on the training set, e.g. between
zero and one.
The transforma... | 9 | 6 | 26 | 4 | 14 | 8 | 2 | 1.29 | 3 | 4 | 0 | 0 | 7 | 9 | 7 | 43 | 308 | 61 | 108 | 30 | 99 | 139 | 64 | 29 | 56 | 4 | 2 | 1 | 12 |
322,839 | 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/preprocessing/_data.py | sklearn.preprocessing._data.Normalizer | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils._param_validation import Interval, Options, StrOptions, validate_params
from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_da... |
class Normalizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Normalize samples individually to unit norm.
Each sample (i.e. each row of the data matrix) with at least one
non zero component is rescaled independently of other samples so
that its norm (l1, l2 or inf) equals one.
This t... | 6 | 3 | 13 | 2 | 5 | 7 | 1 | 3.5 | 3 | 1 | 0 | 0 | 4 | 2 | 4 | 40 | 134 | 26 | 24 | 10 | 18 | 84 | 18 | 9 | 13 | 2 | 2 | 0 | 5 |
322,840 | 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/preprocessing/_data.py | sklearn.preprocessing._data.PowerTransformer | from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
import numpy as np
from scipy import sparse, stats
from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_data
from ..utils.fixes import _yeoj... |
class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Apply a power transform featurewise to make data more Gaussian-like.
Power transforms are a family of parametric, monotonic transformations
that are applied to make data more Gaussian-like. This is useful for
modeling iss... | 16 | 11 | 24 | 4 | 12 | 9 | 2 | 1.23 | 3 | 7 | 1 | 0 | 12 | 5 | 12 | 48 | 413 | 90 | 149 | 46 | 133 | 184 | 109 | 44 | 95 | 7 | 2 | 3 | 31 |
322,841 | 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/preprocessing/_data.py | sklearn.preprocessing._data.QuantileTransformer | from numbers import Integral, Real
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
import numpy as np
from scipy import sparse, stats
from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_d... |
class QuantileTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Transform features using quantiles information.
This method transforms the features to follow a uniform or a normal
distribution. Therefore, for a given feature, this transformation tends
to spread out the most frequen... | 12 | 9 | 30 | 3 | 17 | 11 | 3 | 1.08 | 3 | 5 | 0 | 0 | 10 | 9 | 10 | 46 | 436 | 65 | 180 | 55 | 159 | 195 | 116 | 45 | 105 | 8 | 2 | 3 | 32 |
322,842 | 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/preprocessing/_data.py | sklearn.preprocessing._data.RobustScaler | from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_data
from scipy import sparse, stats
import numpy as np
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils import _array_api,... |
class RobustScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Scale features using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to
the quantile range (defaults to IQR: Interquartile Range).
The IQR is the range between the 1st quar... | 7 | 4 | 30 | 3 | 20 | 7 | 4 | 1.09 | 3 | 4 | 0 | 0 | 5 | 7 | 5 | 41 | 273 | 45 | 109 | 30 | 94 | 119 | 62 | 21 | 56 | 8 | 2 | 3 | 20 |
322,843 | 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/preprocessing/_data.py | sklearn.preprocessing._data.StandardScaler | from ..utils import _array_api, check_array, resample
from ..utils.sparsefuncs import incr_mean_variance_axis, inplace_column_scale, mean_variance_axis, min_max_axis
from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, validate_data
from scipy import sparse, stats
from... |
class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
'''Standardize features by removing the mean and scaling to unit variance.
The standard score of a sample `x` is calculated as:
.. code-block:: text
z = (x - u) / s
where `u` is the mean of the training samples or zero... | 9 | 6 | 40 | 5 | 23 | 13 | 5 | 1.16 | 3 | 2 | 0 | 2 | 7 | 7 | 7 | 43 | 427 | 67 | 167 | 24 | 158 | 194 | 92 | 23 | 84 | 15 | 2 | 3 | 34 |
322,844 | 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/preprocessing/_discretization.py | sklearn.preprocessing._discretization.KBinsDiscretizer | from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils.validation import _check_feature_names_in, _check_sample_weight, check_array, check_is_fitted, validate_data
from numbers import Integral
import warnings
from ..utils._param_validation import Interval, Options, StrOptions
import numpy as np
f... |
class KBinsDiscretizer(TransformerMixin, BaseEstimator):
'''
Bin continuous data into intervals.
Read more in the :ref:`User Guide <preprocessing_discretization>`.
.. versionadded:: 0.20
Parameters
----------
n_bins : int or array-like of shape (n_features,), default=5
The number of... | 8 | 6 | 56 | 8 | 32 | 16 | 6 | 1.02 | 2 | 10 | 2 | 0 | 6 | 10 | 6 | 41 | 525 | 89 | 217 | 64 | 198 | 221 | 121 | 53 | 113 | 17 | 2 | 3 | 33 |
322,845 | 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/preprocessing/_encoders.py | sklearn.preprocessing._encoders.OneHotEncoder | from scipy import sparse
import numpy as np
from ..utils._param_validation import Interval, RealNotInt, StrOptions
from ..utils import _safe_indexing, check_array
from numbers import Integral
from ..utils._missing import is_scalar_nan
from ..utils.validation import _check_feature_names, _check_feature_names_in, _check_... |
class OneHotEncoder(_BaseEncoder):
'''
Encode categorical features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are encoded using a one-hot (aka 'one-of-K' or '... | 13 | 10 | 44 | 5 | 27 | 11 | 6 | 1.08 | 1 | 9 | 0 | 0 | 11 | 12 | 11 | 55 | 787 | 129 | 318 | 99 | 294 | 344 | 196 | 85 | 184 | 18 | 3 | 5 | 62 |
322,846 | 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/preprocessing/_encoders.py | sklearn.preprocessing._encoders.OrdinalEncoder | from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
import numbers
from ..utils.validation import _check_feature_names, _check_feature_names_in, _check_n_features, check_is_fitted
from ..utils._mask import _get_mask
from ..utils._missing import is_scalar_nan
from numbers import Integr... |
class OrdinalEncoder(OneToOneFeatureMixin, _BaseEncoder):
'''
Encode categorical features as an integer array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are converted to ordinal integer... | 6 | 4 | 60 | 9 | 39 | 13 | 9 | 1.18 | 2 | 9 | 0 | 0 | 4 | 8 | 4 | 49 | 445 | 77 | 169 | 54 | 153 | 200 | 96 | 43 | 91 | 19 | 3 | 4 | 35 |
322,847 | 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/preprocessing/_encoders.py | sklearn.preprocessing._encoders._BaseEncoder | from ..utils.validation import _check_feature_names, _check_feature_names_in, _check_n_features, check_is_fitted
import numbers
from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..utils._encode import _check_unknown, _encode, _get_counts, _unique
import numpy as np
from ..utils... |
class _BaseEncoder(TransformerMixin, BaseEstimator):
'''
Base class for encoders that includes the code to categorize and
transform the input features.
'''
def _check_X(self, X, ensure_all_finite=True):
'''
Perform custom check_array:
- convert list of strings to object dty... | 11 | 7 | 47 | 6 | 29 | 12 | 7 | 0.44 | 2 | 14 | 0 | 3 | 9 | 5 | 9 | 44 | 435 | 61 | 261 | 86 | 234 | 114 | 172 | 69 | 162 | 22 | 2 | 4 | 59 |
322,848 | 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/preprocessing/_function_transformer.py | sklearn.preprocessing._function_transformer.FunctionTransformer | from ..utils._repr_html.estimator import _VisualBlock
import numpy as np
from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils._set_output import _get_adapter_from_container, _get_output_config
from ..utils.metaestimators import available_if
from ..utils._param_validation import StrOptions
impor... |
class FunctionTransformer(TransformerMixin, BaseEstimator):
'''Constructs a transformer from an arbitrary callable.
A FunctionTransformer forwards its X (and optionally y) arguments to a
user-defined function or function object and returns the result of this
function. This is useful for stateless trans... | 16 | 9 | 21 | 2 | 12 | 7 | 3 | 1.08 | 2 | 9 | 1 | 0 | 13 | 9 | 13 | 48 | 412 | 66 | 166 | 50 | 139 | 180 | 89 | 37 | 75 | 7 | 2 | 3 | 35 |
322,849 | 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/preprocessing/_label.py | sklearn.preprocessing._label.LabelBinarizer | from ..utils.multiclass import type_of_target, unique_labels
from numbers import Integral
from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils.validation import _num_samples, check_array, check_is_fitted
import scipy.sparse as sp
class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_... |
class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
'''Binarize labels in a one-vs-all fashion.
Several regression and binary classification algorithms are
available in scikit-learn. A simple way to extend these algorithms
to the multi-class classification case is to use ... | 8 | 5 | 25 | 4 | 10 | 11 | 3 | 2.07 | 3 | 3 | 1 | 0 | 6 | 6 | 6 | 41 | 253 | 47 | 67 | 18 | 59 | 139 | 43 | 17 | 36 | 5 | 2 | 1 | 15 |
322,850 | 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/preprocessing/_label.py | sklearn.preprocessing._label.LabelEncoder | from ..utils.validation import _num_samples, check_array, check_is_fitted
from ..utils._array_api import device, get_namespace, xpx
from ..utils import column_or_1d
from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils._encode import _encode, _unique
class LabelEncoder(TransformerMixin, BaseEst... |
class LabelEncoder(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
'''Encode target labels with value between 0 and n_classes-1.
This transformer should be used to encode target values, *i.e.* `y`, and
not the input `X`.
Read more in the :ref:`User Guide <preprocessing_targets>`.
.. v... | 6 | 5 | 18 | 2 | 7 | 8 | 2 | 2.19 | 3 | 3 | 0 | 0 | 5 | 1 | 5 | 40 | 142 | 24 | 37 | 11 | 31 | 81 | 33 | 11 | 27 | 3 | 2 | 1 | 8 |
322,851 | 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/preprocessing/_label.py | sklearn.preprocessing._label.MultiLabelBinarizer | from ..base import BaseEstimator, TransformerMixin, _fit_context
import array
import warnings
from ..utils.validation import _num_samples, check_array, check_is_fitted
import itertools
from collections import defaultdict
import numpy as np
import scipy.sparse as sp
class MultiLabelBinarizer(TransformerMixin, BaseEstim... |
class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
'''Transform between iterable of iterables and a multilabel format.
Although a list of sets or tuples is a very intuitive format for multilabel
data, it is unwieldy to process. This transformer converts between this
... | 11 | 6 | 24 | 3 | 12 | 9 | 3 | 1.13 | 3 | 14 | 1 | 0 | 8 | 4 | 8 | 43 | 267 | 45 | 104 | 34 | 93 | 118 | 77 | 32 | 68 | 5 | 2 | 3 | 24 |
322,852 | 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/preprocessing/_polynomial.py | sklearn.preprocessing._polynomial.PolynomialFeatures | from itertools import combinations_with_replacement as combinations_w_r
from itertools import chain, combinations
from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, _check_sample_weight, check_is_fitted, validate_data
from ..base import BaseEstimator, TransformerMixin, _fit_context
import numpy as np... |
class PolynomialFeatures(TransformerMixin, BaseEstimator):
'''Generate polynomial and interaction features.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional ... | 13 | 6 | 47 | 4 | 33 | 11 | 6 | 0.58 | 2 | 10 | 0 | 0 | 6 | 9 | 8 | 43 | 489 | 56 | 276 | 66 | 257 | 159 | 135 | 53 | 126 | 22 | 2 | 4 | 44 |
322,853 | 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/preprocessing/_polynomial.py | sklearn.preprocessing._polynomial.SplineTransformer | from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils.fixes import parse_version, sp_version
from scipy.interpolate import BSpline
from ..utils._param_validation import Interval, StrOptions
from scipy import sparse
from ..utils import check_array
from ..utils.validation import FLOAT_DTYPES, _che... |
class SplineTransformer(TransformerMixin, BaseEstimator):
'''Generate univariate B-spline bases for features.
Generate a new feature matrix consisting of
`n_splines=n_knots + degree - 1` (`n_knots - 1` for
`extrapolation="periodic"`) spline basis functions
(B-splines) of polynomial order=`degree` f... | 8 | 5 | 88 | 9 | 53 | 26 | 10 | 0.82 | 2 | 8 | 1 | 0 | 4 | 9 | 5 | 40 | 578 | 74 | 277 | 76 | 259 | 228 | 159 | 64 | 153 | 31 | 2 | 5 | 49 |
322,854 | 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/preprocessing/_target_encoder.py | sklearn.preprocessing._target_encoder.TargetEncoder | from ..utils.multiclass import type_of_target
from ..base import OneToOneFeatureMixin, _fit_context
from ._target_encoder_fast import _fit_encoding_fast, _fit_encoding_fast_auto_smooth
from ._encoders import _BaseEncoder
from ..utils._param_validation import Interval, StrOptions
from ..utils.validation import _check_fe... |
class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder):
'''Target Encoder for regression and classification targets.
Each category is encoded based on a shrunk estimate of the average target
values for observations belonging to the category. The encoding scheme mixes
the global target mean with the ta... | 13 | 9 | 33 | 3 | 21 | 9 | 3 | 1 | 2 | 10 | 4 | 0 | 10 | 11 | 10 | 55 | 514 | 71 | 222 | 80 | 186 | 223 | 102 | 54 | 89 | 6 | 3 | 2 | 26 |
322,855 | 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/random_projection.py | sklearn.random_projection.BaseRandomProjection | import warnings
from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
from scipy import linalg
from numbers import Integral, Real
from .utils.validation import check_array, check_is_fitted, validate_data
from .exceptions import DataDimensionalityWarning
from abc import ABCMeta... |
class BaseRandomProjection(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator, metaclass=ABCMeta):
'''Base class for random projections.
Warning: This class should not be used directly.
Use derived classes instead.
'''
@abstractmethod
def __init__(self, n_components='auto', *,... | 10 | 5 | 22 | 4 | 11 | 7 | 2 | 0.57 | 4 | 3 | 1 | 2 | 6 | 8 | 6 | 62 | 161 | 31 | 83 | 32 | 64 | 47 | 41 | 20 | 34 | 6 | 3 | 2 | 13 |
322,856 | 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/random_projection.py | sklearn.random_projection.GaussianRandomProjection | from .utils.validation import check_array, check_is_fitted, validate_data
import numpy as np
from .utils import check_random_state
class GaussianRandomProjection(BaseRandomProjection):
"""Reduce dimensionality through Gaussian random projection.
The components of the random matrix are drawn from N(0, 1 / n_co... |
class GaussianRandomProjection(BaseRandomProjection):
'''Reduce dimensionality through Gaussian random projection.
The components of the random matrix are drawn from N(0, 1 / n_components).
Read more in the :ref:`User Guide <gaussian_random_matrix>`.
.. versionadded:: 0.13
Parameters
----------... | 4 | 3 | 19 | 2 | 10 | 7 | 1 | 2.8 | 1 | 1 | 0 | 0 | 3 | 0 | 3 | 65 | 143 | 29 | 30 | 12 | 19 | 84 | 10 | 5 | 6 | 1 | 4 | 0 | 3 |
322,857 | 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/random_projection.py | sklearn.random_projection.SparseRandomProjection | from numbers import Integral, Real
from .utils import check_random_state
from .utils._param_validation import Interval, StrOptions, validate_params
import numpy as np
from .utils.validation import check_array, check_is_fitted, validate_data
from .utils.extmath import safe_sparse_dot
class SparseRandomProjection(BaseRa... |
class SparseRandomProjection(BaseRandomProjection):
'''Reduce dimensionality through sparse random projection.
Sparse random matrix is an alternative to dense random
projection matrix that guarantees similar embedding quality while being
much more memory efficient and allowing faster computation of the... | 4 | 3 | 22 | 3 | 11 | 8 | 1 | 3.15 | 1 | 1 | 0 | 0 | 3 | 3 | 3 | 65 | 210 | 44 | 40 | 18 | 27 | 126 | 14 | 9 | 10 | 1 | 4 | 0 | 3 |
322,858 | 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/semi_supervised/_label_propagation.py | sklearn.semi_supervised._label_propagation.BaseLabelPropagation | from ..utils._param_validation import Interval, StrOptions
from ..metrics.pairwise import rbf_kernel
from ..base import BaseEstimator, ClassifierMixin, _fit_context
from scipy import sparse
import numpy as np
from ..utils.multiclass import check_classification_targets
from abc import ABCMeta, abstractmethod
from ..util... |
class BaseLabelPropagation(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):
'''Base class for label propagation module.
Parameters
----------
kernel : {'knn', 'rbf'} or callable, default='rbf'
String identifier for kernel function to use or the kernel function
itself. Only 'rbf'... | 10 | 4 | 31 | 4 | 19 | 8 | 3 | 0.57 | 3 | 5 | 2 | 2 | 7 | 13 | 7 | 60 | 267 | 41 | 144 | 49 | 124 | 82 | 85 | 37 | 77 | 8 | 3 | 2 | 21 |
322,859 | 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/semi_supervised/_label_propagation.py | sklearn.semi_supervised._label_propagation.LabelPropagation | import numpy as np
from scipy import sparse
class LabelPropagation(BaseLabelPropagation):
"""Label Propagation classifier.
Read more in the :ref:`User Guide <label_propagation>`.
Parameters
----------
kernel : {'knn', 'rbf'} or callable, default='rbf'
String identifier for kernel function... |
class LabelPropagation(BaseLabelPropagation):
'''Label Propagation classifier.
Read more in the :ref:`User Guide <label_propagation>`.
Parameters
----------
kernel : {'knn', 'rbf'} or callable, default='rbf'
String identifier for kernel function to use or the kernel function
itself.... | 4 | 3 | 18 | 1 | 10 | 6 | 2 | 2.37 | 1 | 1 | 0 | 0 | 3 | 1 | 3 | 63 | 146 | 28 | 35 | 18 | 22 | 83 | 17 | 9 | 13 | 3 | 4 | 1 | 5 |
322,860 | 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/semi_supervised/_label_propagation.py | sklearn.semi_supervised._label_propagation.LabelSpreading | from scipy import sparse
from numbers import Integral, Real
from ..utils.fixes import laplacian as csgraph_laplacian
from ..utils._param_validation import Interval, StrOptions
class LabelSpreading(BaseLabelPropagation):
"""LabelSpreading model for semi-supervised learning.
This model is similar to the basic L... |
class LabelSpreading(BaseLabelPropagation):
'''LabelSpreading model for semi-supervised learning.
This model is similar to the basic Label Propagation algorithm,
but uses affinity matrix based on the normalized graph Laplacian
and soft clamping across the labels.
Read more in the :ref:`User Guide <... | 3 | 2 | 18 | 0 | 17 | 2 | 2 | 2.08 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 62 | 138 | 25 | 37 | 20 | 24 | 77 | 18 | 10 | 15 | 3 | 4 | 1 | 4 |
322,861 | 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/semi_supervised/_self_training.py | sklearn.semi_supervised._self_training.SelfTrainingClassifier | from ..utils import Bunch, get_tags, safe_mask
import numpy as np
from ..utils.validation import _estimator_has, check_is_fitted, validate_data
import warnings
from ..base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, _fit_context, clone
from ..utils.metaestimators import available_if
from numbers import I... |
class SelfTrainingClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
'''Self-training classifier.
This :term:`metaestimator` allows a given supervised classifier to function as a
semi-supervised classifier, allowing it to learn from unlabeled data. It
does this by iteratively predicting ps... | 17 | 9 | 43 | 6 | 22 | 15 | 3 | 1.06 | 3 | 8 | 3 | 0 | 10 | 13 | 10 | 43 | 594 | 97 | 241 | 58 | 213 | 256 | 113 | 41 | 102 | 12 | 2 | 3 | 30 |
322,862 | 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/svm/_base.py | sklearn.svm._base.BaseLibSVM | import scipy.sparse as sp
from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight
import warnings
from ..base import BaseEstimator, ClassifierMixin, _fit_context
from abc import ABCMeta, abstractmethod
from . import _libsvm as libsvm
from ..utils.validation import _check_large_sparse, _c... |
class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
'''Base class for estimators that use libsvm as backing library.
This implements support vector machine classification and regression.
Parameter documentation is in the derived `SVC` class.
'''
@abstractmethod
def __init__(self, kernel, de... | 23 | 8 | 31 | 3 | 23 | 5 | 4 | 0.23 | 2 | 11 | 3 | 4 | 18 | 32 | 18 | 69 | 623 | 83 | 441 | 106 | 401 | 101 | 191 | 77 | 172 | 22 | 3 | 2 | 67 |
322,863 | 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/svm/_base.py | sklearn.svm._base.BaseSVC | from . import _libsvm as libsvm
from ..utils.validation import _check_large_sparse, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted, validate_data
from ..utils._param_validation import Interval, StrOptions
from ..exceptions import ConvergenceWarning, NotFittedError
from . import _libsvm_spa... |
class BaseSVC(ClassifierMixin, BaseLibSVM, metaclass=ABCMeta):
'''ABC for LibSVM-based classifiers.'''
@abstractmethod
def __init__(self, kernel, degree, gamma, coef0, tol, C, nu, shrinking, probability, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state, break_ties):
... | 19 | 7 | 22 | 2 | 13 | 7 | 2 | 0.54 | 3 | 4 | 1 | 2 | 13 | 15 | 13 | 84 | 313 | 40 | 177 | 65 | 140 | 96 | 75 | 32 | 61 | 3 | 4 | 2 | 25 |
322,864 | 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/svm/_classes.py | sklearn.svm._classes.LinearSVC | from numbers import Integral, Real
from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMixin
from ..utils.multiclass import check_classification_targets
from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
from ..utils._param_validation import Interval, StrOption... |
class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
'''Linear Support Vector Classification.
Similar to SVC with parameter kernel='linear', but implemented in terms of
liblinear rather than libsvm, so it has more flexibility in the choice of
penalties and loss functions and should s... | 5 | 2 | 34 | 3 | 24 | 7 | 2 | 2.11 | 3 | 1 | 0 | 1 | 3 | 16 | 3 | 41 | 321 | 50 | 87 | 39 | 67 | 184 | 32 | 23 | 28 | 3 | 2 | 2 | 5 |
322,865 | 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/svm/_classes.py | sklearn.svm._classes.LinearSVR | from ..utils.validation import _num_samples, validate_data
from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
import numpy as np
from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMix... |
class LinearSVR(RegressorMixin, LinearModel):
'''Linear Support Vector Regression.
Similar to SVR with parameter kernel='linear', but implemented in terms of
liblinear rather than libsvm, so it has more flexibility in the choice of
penalties and loss functions and should scale better to large numbers o... | 5 | 2 | 30 | 3 | 20 | 8 | 1 | 1.89 | 2 | 1 | 0 | 0 | 3 | 13 | 3 | 60 | 254 | 41 | 74 | 34 | 56 | 140 | 25 | 20 | 21 | 1 | 4 | 0 | 3 |
322,866 | 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/svm/_classes.py | sklearn.svm._classes.NuSVC | from ..utils._param_validation import Interval, StrOptions
from numbers import Integral, Real
from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
class NuSVC(BaseSVC):
"""Nu-Support Vector Classification.
Similar to SVC but uses a parameter to control the number of support
v... |
class NuSVC(BaseSVC):
'''Nu-Support Vector Classification.
Similar to SVC but uses a parameter to control the number of support
vectors.
The implementation is based on libsvm.
Read more in the :ref:`User Guide <svm_classification>`.
Parameters
----------
nu : float, default=0.5
... | 2 | 1 | 37 | 0 | 37 | 0 | 1 | 3.75 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 85 | 261 | 52 | 44 | 22 | 24 | 165 | 6 | 4 | 4 | 1 | 5 | 0 | 1 |
322,867 | 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/svm/_classes.py | sklearn.svm._classes.NuSVR | from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
class NuSVR(RegressorMixin, BaseLibSVM):
"""Nu Support Vector Regression.
Similar to NuSVC, for regression, uses a parameter nu to control
the num... |
class NuSVR(RegressorMixin, BaseLibSVM):
'''Nu Support Vector Regression.
Similar to NuSVC, for regression, uses a parameter nu to control
the number of support vectors. However, unlike NuSVC, where nu
replaces C, here nu replaces the parameter epsilon of epsilon-SVR.
The implementation is based on... | 2 | 1 | 32 | 0 | 32 | 0 | 1 | 2.95 | 2 | 1 | 0 | 0 | 1 | 0 | 1 | 72 | 185 | 39 | 37 | 19 | 21 | 109 | 7 | 5 | 5 | 1 | 4 | 1 | 1 |
322,868 | 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/svm/_classes.py | sklearn.svm._classes.OneClassSVM | from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
import numpy as np
from ..utils.validation import _num_samples, validate_data
from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
class OneClassSVM(OutlierMixin, BaseLibSVM):
"""Unsupervised Outlier Detectio... |
class OneClassSVM(OutlierMixin, BaseLibSVM):
'''Unsupervised Outlier Detection.
Estimate the support of a high-dimensional distribution.
The implementation is based on libsvm.
Read more in the :ref:`User Guide <outlier_detection>`.
Parameters
----------
kernel : {'linear', 'poly', 'rbf', 's... | 6 | 5 | 22 | 3 | 9 | 11 | 1 | 3.17 | 2 | 1 | 0 | 0 | 5 | 1 | 5 | 76 | 255 | 55 | 48 | 25 | 29 | 152 | 19 | 12 | 13 | 1 | 4 | 1 | 5 |
322,869 | 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/svm/_classes.py | sklearn.svm._classes.SVC | from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
class SVC(BaseSVC):
"""C-Support Vector Classification.
The implementation is based on libsvm. The fit time scales at least
quadratically with the number of samples and may be impractical
beyond tens of thousands of sam... |
class SVC(BaseSVC):
'''C-Support Vector Classification.
The implementation is based on libsvm. The fit time scales at least
quadratically with the number of samples and may be impractical
beyond tens of thousands of samples. For large datasets
consider using :class:`~sklearn.svm.LinearSVC` or
:... | 2 | 1 | 37 | 0 | 37 | 0 | 1 | 4.69 | 1 | 1 | 0 | 2 | 1 | 0 | 1 | 85 | 275 | 53 | 39 | 21 | 19 | 183 | 4 | 3 | 2 | 1 | 5 | 0 | 1 |
322,870 | 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/svm/_classes.py | sklearn.svm._classes.SVR | from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
class SVR(RegressorMixin, BaseLibSVM):
"""Epsilon-Support Vector Regression.
The free parameters in the model are C and epsilon.
The implementati... |
class SVR(RegressorMixin, BaseLibSVM):
'''Epsilon-Support Vector Regression.
The free parameters in the model are C and epsilon.
The implementation is based on libsvm. The fit time complexity
is more than quadratic with the number of samples which makes it hard
to scale to datasets with more than a... | 2 | 1 | 32 | 0 | 32 | 0 | 1 | 3.14 | 2 | 1 | 0 | 2 | 1 | 0 | 1 | 72 | 192 | 39 | 37 | 19 | 21 | 116 | 7 | 5 | 5 | 1 | 4 | 1 | 1 |
322,871 | 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/tree/_classes.py | sklearn.tree._classes.BaseDecisionTree | from ..utils import Bunch, check_random_state, compute_sample_weight
import numbers
from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context, clone, is_classifier
from ..utils.multiclass import check_classification_targets
from numbers import Integral, Real
from ..utils._param_v... |
class BaseDecisionTree(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
'''Base class for decision trees.
Warning: This class should not be used directly.
Use derived classes instead.
'''
@abstractmethod
def __init__(self, *, criterion, splitter, max_depth, min_samples_split, min_sample... | 17 | 11 | 40 | 6 | 23 | 11 | 5 | 0.47 | 3 | 10 | 1 | 2 | 14 | 19 | 14 | 66 | 606 | 97 | 348 | 96 | 308 | 164 | 203 | 71 | 188 | 41 | 3 | 3 | 66 |
322,872 | 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/tree/_classes.py | sklearn.tree._classes.DecisionTreeClassifier | from ._criterion import Criterion
from sklearn.utils import metadata_routing
from ..utils.validation import _assert_all_finite_element_wise, _check_n_features, _check_sample_weight, assert_all_finite, check_is_fitted, validate_data
import numpy as np
from ..utils._param_validation import Hidden, Interval, RealNotInt, S... |
class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
'''A decision tree classifier.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : {"gini", "entropy", "log_loss"}, default="gini"
The function to measure the quality of a split. Supported criteria are... | 7 | 4 | 29 | 3 | 14 | 12 | 2 | 3.13 | 2 | 2 | 0 | 4 | 5 | 2 | 5 | 73 | 405 | 79 | 79 | 34 | 56 | 247 | 33 | 17 | 27 | 3 | 4 | 2 | 9 |
322,873 | 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/tree/_classes.py | sklearn.tree._classes.DecisionTreeRegressor | from ._criterion import Criterion
from sklearn.utils import metadata_routing
from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context, clone, is_classifier
from ..utils._param_validation import Hidden, Interval, RealNotInt, StrOptions
import numpy as np
class DecisionTreeRegres... |
class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
'''A decision tree regressor.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : {"squared_error", "friedman_mse", "absolute_error", "poisson"}, default="squared_error"
The function to measu... | 6 | 3 | 26 | 2 | 14 | 10 | 1 | 3.12 | 2 | 1 | 0 | 3 | 4 | 2 | 4 | 72 | 340 | 64 | 67 | 26 | 46 | 209 | 19 | 10 | 14 | 1 | 4 | 0 | 4 |
322,874 | 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/tree/_classes.py | sklearn.tree._classes.ExtraTreeClassifier | class ExtraTreeClassifier(DecisionTreeClassifier):
"""An extremely randomized tree classifier.
Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_featur... | class ExtraTreeClassifier(DecisionTreeClassifier):
'''An extremely randomized tree classifier.
Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_feature... | 3 | 1 | 22 | 0 | 21 | 1 | 1 | 4.37 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 75 | 287 | 56 | 43 | 21 | 24 | 188 | 9 | 5 | 6 | 1 | 5 | 0 | 2 |
322,875 | 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/tree/_classes.py | sklearn.tree._classes.ExtraTreeRegressor | class ExtraTreeRegressor(DecisionTreeRegressor):
"""An extremely randomized tree regressor.
Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_features`... | class ExtraTreeRegressor(DecisionTreeRegressor):
'''An extremely randomized tree regressor.
Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_features` ... | 3 | 1 | 21 | 0 | 20 | 1 | 1 | 4.15 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 74 | 256 | 50 | 40 | 20 | 22 | 166 | 8 | 5 | 5 | 1 | 5 | 0 | 2 |
322,876 | 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/tree/_export.py | sklearn.tree._export.Sentinel | class Sentinel:
def __repr__(self):
return '"tree.dot"' | class Sentinel:
def __repr__(self):
pass | 2 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 3 | 0 | 3 | 2 | 1 | 0 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
322,877 | 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/tree/_export.py | sklearn.tree._export._BaseTreeExporter | import numpy as np
from . import DecisionTreeClassifier, DecisionTreeRegressor, _criterion, _tree
from collections.abc import Iterable
class _BaseTreeExporter:
def __init__(self, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, impurity=True, node_ids=False, proportion=False, round... |
class _BaseTreeExporter:
def __init__(self, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, impurity=True, node_ids=False, proportion=False, rounded=False, precision=3, fontsize=None):
pass
def get_color(self, value):
pass
def get_fill_color(self, tree, n... | 6 | 0 | 38 | 2 | 29 | 7 | 7 | 0.24 | 0 | 6 | 0 | 2 | 5 | 11 | 5 | 5 | 196 | 14 | 148 | 42 | 129 | 35 | 95 | 29 | 89 | 23 | 0 | 2 | 34 |
322,878 | 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/tree/_export.py | sklearn.tree._export._DOTTreeExporter | from . import DecisionTreeClassifier, DecisionTreeRegressor, _criterion, _tree
import numpy as np
class _DOTTreeExporter(_BaseTreeExporter):
def __init__(self, out_file=SENTINEL, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, leaves_parallel=False, impurity=True, node_ids=False, ... |
class _DOTTreeExporter(_BaseTreeExporter):
def __init__(self, out_file=SENTINEL, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, leaves_parallel=False, impurity=True, node_ids=False, proportion=False, rotate=False, rounded=False, special_characters=False, precision=3, fontname='he... | 7 | 0 | 28 | 3 | 23 | 4 | 5 | 0.15 | 1 | 3 | 0 | 0 | 6 | 8 | 6 | 11 | 176 | 22 | 136 | 37 | 112 | 21 | 79 | 20 | 72 | 12 | 1 | 4 | 28 |
322,879 | 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/tree/_export.py | sklearn.tree._export._MPLTreeExporter | from ._reingold_tilford import Tree, buchheim
from . import DecisionTreeClassifier, DecisionTreeRegressor, _criterion, _tree
class _MPLTreeExporter(_BaseTreeExporter):
def __init__(self, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, impurity=True, node_ids=False, proportion=Fals... |
class _MPLTreeExporter(_BaseTreeExporter):
def __init__(self, max_depth=None, feature_names=None, class_names=None, label='all', filled=False, impurity=True, node_ids=False, proportion=False, rounded=False, precision=3, fontsize=None):
pass
def _make_tree(self, node_id, et, criterion, depth=0):
... | 5 | 0 | 43 | 5 | 33 | 6 | 4 | 0.17 | 1 | 4 | 2 | 0 | 4 | 6 | 4 | 9 | 175 | 21 | 133 | 49 | 112 | 22 | 71 | 36 | 63 | 8 | 1 | 4 | 17 |
322,880 | 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/tree/_reingold_tilford.py | sklearn.tree._reingold_tilford.DrawTree | import numpy as np
class DrawTree:
def __init__(self, tree, parent=None, depth=0, number=1):
self.x = -1.0
self.y = depth
self.tree = tree
self.children = [DrawTree(c, self, depth + 1, i + 1) for i, c in enumerate(tree.children)]
self.parent = parent
self.thread = N... |
class DrawTree:
def __init__(self, tree, parent=None, depth=0, number=1):
pass
def left(self):
pass
def right(self):
pass
def lbrother(self):
pass
def get_lmost_sibling(self):
pass
def __str__(self):
pass
def __repr__(self):
pas... | 9 | 0 | 5 | 0 | 5 | 0 | 2 | 0.02 | 0 | 1 | 0 | 0 | 8 | 12 | 8 | 8 | 50 | 8 | 41 | 24 | 32 | 1 | 38 | 24 | 29 | 4 | 0 | 3 | 12 |
322,881 | 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/tree/_reingold_tilford.py | sklearn.tree._reingold_tilford.Tree | class Tree:
def __init__(self, label='', node_id=-1, *children):
self.label = label
self.node_id = node_id
if children:
self.children = children
else:
self.children = [] | class Tree:
def __init__(self, label='', node_id=-1, *children):
pass | 2 | 0 | 7 | 0 | 7 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 1 | 8 | 0 | 8 | 5 | 6 | 0 | 7 | 5 | 5 | 2 | 0 | 1 | 2 |
322,882 | 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/utils/_available_if.py | sklearn.utils._available_if._AvailableIfDescriptor | from types import MethodType
from functools import update_wrapper, wraps
class _AvailableIfDescriptor:
"""Implements a conditional property using the descriptor protocol.
Using this class to create a decorator will raise an ``AttributeError``
if check(self) returns a falsey value. Note that if check raise... |
class _AvailableIfDescriptor:
'''Implements a conditional property using the descriptor protocol.
Using this class to create a decorator will raise an ``AttributeError``
if check(self) returns a falsey value. Note that if check raises an error
this will also result in hasattr returning false.
See h... | 6 | 1 | 9 | 1 | 7 | 1 | 2 | 0.46 | 0 | 2 | 0 | 0 | 3 | 3 | 3 | 3 | 47 | 9 | 26 | 12 | 20 | 12 | 22 | 10 | 17 | 3 | 0 | 1 | 7 |
322,883 | 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/utils/_bunch.py | sklearn.utils._bunch.Bunch | import warnings
class Bunch(dict):
"""Container object exposing keys as attributes.
Bunch objects are sometimes used as an output for functions and methods.
They extend dictionaries by enabling values to be accessed by key,
`bunch["value_key"]`, or by an attribute, `bunch.value_key`.
Examples
... |
class Bunch(dict):
'''Container object exposing keys as attributes.
Bunch objects are sometimes used as an output for functions and methods.
They extend dictionaries by enabling values to be accessed by key,
`bunch["value_key"]`, or by an attribute, `bunch.value_key`.
Examples
--------
>>> ... | 8 | 2 | 5 | 0 | 3 | 1 | 1 | 1.16 | 1 | 4 | 0 | 0 | 7 | 0 | 7 | 34 | 64 | 10 | 25 | 8 | 17 | 29 | 22 | 8 | 14 | 2 | 2 | 1 | 9 |
322,884 | 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/utils/_encode.py | sklearn.utils._encode.MissingValues | import numpy as np
from typing import NamedTuple
class MissingValues(NamedTuple):
"""Data class for missing data information"""
nan: bool
none: bool
def to_list(self):
"""Convert tuple to a list where None is always first."""
output = []
if self.none:
output.append(... |
class MissingValues(NamedTuple):
'''Data class for missing data information'''
def to_list(self):
'''Convert tuple to a list where None is always first.'''
pass | 2 | 2 | 8 | 0 | 7 | 1 | 3 | 0.2 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 14 | 2 | 10 | 3 | 8 | 2 | 10 | 3 | 8 | 3 | 1 | 1 | 3 |
322,885 | 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/utils/_encode.py | sklearn.utils._encode._NaNCounter | from ._missing import is_scalar_nan
from collections import Counter
class _NaNCounter(Counter):
"""Counter with support for nan values."""
def __init__(self, items):
super().__init__(self._generate_items(items))
def _generate_items(self, items):
"""Generate items without nans. Stores the ... |
class _NaNCounter(Counter):
'''Counter with support for nan values.'''
def __init__(self, items):
pass
def _generate_items(self, items):
'''Generate items without nans. Stores the nan counts separately.'''
pass
def __missing__(self, key):
pass | 4 | 2 | 5 | 0 | 5 | 0 | 2 | 0.13 | 1 | 2 | 0 | 0 | 3 | 1 | 3 | 59 | 20 | 3 | 15 | 6 | 11 | 2 | 15 | 6 | 11 | 4 | 3 | 2 | 7 |
322,886 | 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/utils/_encode.py | sklearn.utils._encode._nandict | from ._missing import is_scalar_nan
class _nandict(dict):
"""Dictionary with support for nans."""
def __init__(self, mapping):
super().__init__(mapping)
for key, value in mapping.items():
if is_scalar_nan(key):
self.nan_value = value
break
def _... |
class _nandict(dict):
'''Dictionary with support for nans.'''
def __init__(self, mapping):
pass
def __missing__(self, key):
pass | 3 | 1 | 5 | 0 | 5 | 0 | 3 | 0.09 | 1 | 2 | 0 | 0 | 2 | 1 | 2 | 29 | 14 | 2 | 11 | 5 | 8 | 1 | 11 | 5 | 8 | 3 | 2 | 2 | 5 |
322,887 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests.MetadataRequest | class MetadataRequest:
"""Contains the metadata request info of a consumer.
Instances of `MethodMetadataRequest` are used in this class for each
available method under `metadatarequest.{method}`.
Consumer-only classes such as simple estimators return a serialized
version of this class as the outpu... | class MetadataRequest:
'''Contains the metadata request info of a consumer.
Instances of `MethodMetadataRequest` are used in this class for each
available method under `metadatarequest.{method}`.
Consumer-only classes such as simple estimators return a serialized
version of this class as the output ... | 10 | 6 | 16 | 2 | 6 | 8 | 2 | 1.67 | 0 | 6 | 1 | 0 | 9 | 1 | 9 | 9 | 173 | 34 | 52 | 23 | 42 | 87 | 39 | 23 | 29 | 4 | 0 | 2 | 15 |
322,888 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests.MetadataRouter | from copy import deepcopy
from ._bunch import Bunch
class MetadataRouter:
"""Coordinates metadata routing for a :term:`router` object.
This class is used by :term:`meta-estimators` or functions that can route metadata,
to handle their metadata routing. Routing information is stored in a
dictionary-lik... |
class MetadataRouter:
'''Coordinates metadata routing for a :term:`router` object.
This class is used by :term:`meta-estimators` or functions that can route metadata,
to handle their metadata routing. Routing information is stored in a
dictionary-like structure of the form ``{"object_name":
RouterM... | 13 | 9 | 26 | 4 | 11 | 11 | 3 | 1.13 | 0 | 7 | 2 | 0 | 12 | 3 | 12 | 12 | 347 | 58 | 136 | 39 | 123 | 153 | 86 | 39 | 73 | 5 | 0 | 3 | 37 |
322,889 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests.MethodMapping | class MethodMapping:
"""Stores the mapping between caller and callee methods for a :term:`router`.
This class is primarily used in a ``get_metadata_routing()`` of a router
object when defining the mapping between the router's methods and a sub-object (a
sub-estimator or a scorer).
Iterating throug... | class MethodMapping:
'''Stores the mapping between caller and callee methods for a :term:`router`.
This class is primarily used in a ``get_metadata_routing()`` of a router
object when defining the mapping between the router's methods and a sub-object (a
sub-estimator or a scorer).
Iterating through ... | 7 | 3 | 8 | 1 | 4 | 3 | 2 | 0.96 | 0 | 3 | 0 | 0 | 6 | 1 | 6 | 6 | 67 | 14 | 27 | 10 | 20 | 26 | 21 | 10 | 14 | 3 | 0 | 1 | 9 |
322,890 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests.MethodMetadataRequest | from ..exceptions import UnsetMetadataPassedError
from warnings import warn
from ._bunch import Bunch
class MethodMetadataRequest:
"""Container for metadata requests associated with a single method.
Instances of this class get used within a :class:`MetadataRequest` - one per each
public method (`fit`, `tr... |
class MethodMetadataRequest:
'''Container for metadata requests associated with a single method.
Instances of this class get used within a :class:`MetadataRequest` - one per each
public method (`fit`, `transform`, ...) that its owning consumer has.
.. versionadded:: 1.3
Parameters
----------
... | 12 | 8 | 20 | 2 | 11 | 7 | 3 | 0.72 | 0 | 6 | 2 | 0 | 10 | 3 | 10 | 10 | 230 | 37 | 112 | 32 | 95 | 81 | 58 | 25 | 47 | 8 | 0 | 2 | 27 |
322,891 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests.RequestMethod | from typing import TYPE_CHECKING, Optional, Union
import inspect
class RequestMethod:
"""
Descriptor for defining `set_{method}_request` methods in estimators.
.. versionadded:: 1.3
Parameters
----------
name : str
The name of the method for which the request function should be
... |
class RequestMethod:
'''
Descriptor for defining `set_{method}_request` methods in estimators.
.. versionadded:: 1.3
Parameters
----------
name : str
The name of the method for which the request function should be
created, e.g. ``"fit"`` would create a ``set_fit_request`` functi... | 4 | 2 | 43 | 5 | 31 | 8 | 3 | 0.58 | 0 | 7 | 0 | 0 | 2 | 3 | 2 | 2 | 118 | 17 | 64 | 14 | 60 | 37 | 34 | 14 | 30 | 7 | 0 | 2 | 10 |
322,892 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests._MetadataRequester | import inspect
from typing import TYPE_CHECKING, Optional, Union
class _MetadataRequester:
"""Mixin class for adding metadata request functionality.
``BaseEstimator`` inherits from this Mixin.
.. versionadded:: 1.3
"""
if TYPE_CHECKING:
def set_fit_request(self, **kwargs):
pa... |
class _MetadataRequester:
'''Mixin class for adding metadata request functionality.
``BaseEstimator`` inherits from this Mixin.
.. versionadded:: 1.3
'''
def set_fit_request(self, **kwargs):
pass
def set_partial_fit_request(self, **kwargs):
pass
... | 18 | 6 | 10 | 1 | 4 | 5 | 2 | 1.23 | 0 | 7 | 3 | 9 | 13 | 0 | 15 | 15 | 180 | 25 | 70 | 31 | 62 | 86 | 66 | 29 | 50 | 6 | 0 | 3 | 28 |
322,893 | 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/utils/_metadata_requests.py | sklearn.utils._metadata_requests._RoutingNotSupportedMixin | class _RoutingNotSupportedMixin:
"""A mixin to be used to remove the default `get_metadata_routing`.
This is used in meta-estimators where metadata routing is not yet
implemented.
This also makes it clear in our rendered documentation that this method
cannot be used.
"""
def get_metadata_... | class _RoutingNotSupportedMixin:
'''A mixin to be used to remove the default `get_metadata_routing`.
This is used in meta-estimators where metadata routing is not yet
implemented.
This also makes it clear in our rendered documentation that this method
cannot be used.
'''
def get_metadata_ro... | 2 | 2 | 7 | 1 | 4 | 2 | 1 | 1.6 | 0 | 1 | 0 | 3 | 1 | 0 | 1 | 1 | 17 | 4 | 5 | 2 | 3 | 8 | 3 | 2 | 1 | 1 | 0 | 0 | 1 |
322,894 | 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/utils/_mocking.py | sklearn.utils._mocking.ArraySlicingWrapper | class ArraySlicingWrapper:
"""
Parameters
----------
array
"""
def __init__(self, array):
self.array = array
def __getitem__(self, aslice):
return MockDataFrame(self.array[aslice]) | class ArraySlicingWrapper:
'''
Parameters
----------
array
'''
def __init__(self, array):
pass
def __getitem__(self, aslice):
pass | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 2 | 1 | 2 | 2 | 12 | 2 | 5 | 4 | 2 | 5 | 5 | 4 | 2 | 1 | 0 | 0 | 2 |
322,895 | 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/utils/_mocking.py | sklearn.utils._mocking.CheckingClassifier | import numpy as np
from .validation import _check_sample_weight, _num_samples, check_array, check_is_fitted, check_random_state
from ..base import BaseEstimator, ClassifierMixin
class CheckingClassifier(ClassifierMixin, BaseEstimator):
"""Dummy classifier to test pipelining and meta-estimators.
Checks some pr... | null | 9 | 7 | 25 | 2 | 13 | 11 | 4 | 1.3 | 2 | 5 | 0 | 1 | 8 | 11 | 8 | 41 | 276 | 41 | 102 | 46 | 81 | 133 | 78 | 34 | 69 | 8 | 2 | 3 | 28 |
322,896 | 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/utils/_mocking.py | sklearn.utils._mocking.MockDataFrame | class MockDataFrame:
"""
Parameters
----------
array
"""
def __init__(self, array):
self.array = array
self.values = array
self.shape = array.shape
self.ndim = array.ndim
self.iloc = ArraySlicingWrapper(array)
def __len__(self):
return len(se... | class MockDataFrame:
'''
Parameters
----------
array
'''
def __init__(self, array):
pass
def __len__(self):
pass
def __array__(self, dtype=None):
pass
def __eq__(self, other):
pass
def __ne__(self, other):
pass
def take(self, indi... | 7 | 1 | 3 | 0 | 3 | 1 | 1 | 0.59 | 0 | 1 | 1 | 0 | 6 | 5 | 6 | 6 | 34 | 7 | 17 | 12 | 10 | 10 | 17 | 12 | 10 | 1 | 0 | 0 | 6 |
322,897 | 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/utils/_mocking.py | sklearn.utils._mocking.NoSampleWeightWrapper | from ..base import BaseEstimator, ClassifierMixin
class NoSampleWeightWrapper(BaseEstimator):
"""Wrap estimator which will not expose `sample_weight`.
Parameters
----------
est : estimator, default=None
The estimator to wrap.
"""
def __init__(self, est=None):
self.est = est
... |
class NoSampleWeightWrapper(BaseEstimator):
'''Wrap estimator which will not expose `sample_weight`.
Parameters
----------
est : estimator, default=None
The estimator to wrap.
'''
def __init__(self, est=None):
pass
def fit(self, X, y):
pass
def predict(self, X... | 6 | 1 | 2 | 0 | 2 | 0 | 1 | 0.46 | 1 | 1 | 0 | 0 | 5 | 1 | 5 | 36 | 25 | 6 | 13 | 8 | 7 | 6 | 13 | 8 | 7 | 1 | 2 | 0 | 5 |
322,898 | 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/utils/_mocking.py | sklearn.utils._mocking._MockEstimatorOnOffPrediction | from .metaestimators import available_if
from ..base import BaseEstimator, ClassifierMixin
import numpy as np
class _MockEstimatorOnOffPrediction(BaseEstimator):
"""Estimator for which we can turn on/off the prediction methods.
Parameters
----------
response_methods: list of {"predict", "p... |
class _MockEstimatorOnOffPrediction(BaseEstimator):
'''Estimator for which we can turn on/off the prediction methods.
Parameters
----------
response_methods: list of {"predict", "predict_proba", "decision_function"}, default=None
List containing the response implemented by the estim... | 9 | 1 | 2 | 0 | 2 | 0 | 1 | 0.73 | 1 | 0 | 0 | 0 | 5 | 2 | 5 | 36 | 32 | 6 | 15 | 11 | 6 | 11 | 12 | 8 | 6 | 1 | 2 | 0 | 5 |
322,899 | 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/utils/_param_validation.py | sklearn.utils._param_validation.HasMethods | class HasMethods(_Constraint):
"""Constraint representing objects that expose specific methods.
It is useful for parameters following a protocol and where we don't want to impose
an affiliation to a specific module or class.
Parameters
----------
methods : str or list of str
The method... | class HasMethods(_Constraint):
'''Constraint representing objects that expose specific methods.
It is useful for parameters following a protocol and where we don't want to impose
an affiliation to a specific module or class.
Parameters
----------
methods : str or list of str
The method(s... | 5 | 1 | 5 | 0 | 5 | 0 | 2 | 0.38 | 1 | 2 | 0 | 0 | 3 | 1 | 3 | 26 | 34 | 5 | 21 | 10 | 13 | 8 | 13 | 6 | 9 | 2 | 5 | 1 | 5 |
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