How to use the imblearn.under_sampling.base.BaseCleaningSampler function in imblearn

axis=0,
            )

        self.sample_indices_ = idx_under

        return _safe_indexing(X, idx_under), _safe_indexing(y, idx_under)

    def _more_tags(self):
        return {"sample_indices": True}


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class RepeatedEditedNearestNeighbours(BaseCleaningSampler):
    """Undersample based on the repeated edited nearest neighbour method.

    This method will repeat several time the ENN algorithm.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    n_neighbors : int or object, default=3
        If ``int``, size of the neighbourhood to consider to compute the
        nearest neighbors. If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the nearest-neighbors.

import numpy as np
from scipy.stats import mode

from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ._edited_nearest_neighbours import EditedNearestNeighbours
from ...utils import check_neighbors_object
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring

SEL_KIND = ("all", "mode")


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class NeighbourhoodCleaningRule(BaseCleaningSampler):
    """Undersample based on the neighbourhood cleaning rule.

    This class uses ENN and a k-NN to remove noisy samples from the datasets.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    n_neighbors : int or object, default=3
        If ``int``, size of the neighbourhood to consider to compute the
        nearest neighbors. If object, an estimator that inherits from

# Authors: Guillaume Lemaitre 
#          Fernando Nogueira
#          Christos Aridas
# License: MIT

import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class TomekLinks(BaseCleaningSampler):
    """Under-sampling by removing Tomek's links.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {n_jobs}

    Attributes
    ----------
    sample_indices_ : ndarray of shape (n_new_samples)

# License: MIT

import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class TomekLinks(BaseCleaningSampler):
    """Under-sampling by removing Tomek's links.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {n_jobs}

    Attributes
    ----------
    sample_indices_ : ndarray of shape (n_new_samples)
        Indices of the samples selected.

        .. versionadded:: 0.4

from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import check_neighbors_object
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring

SEL_KIND = ("all", "mode")


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class EditedNearestNeighbours(BaseCleaningSampler):
    """Undersample based on the edited nearest neighbour method.

    This method will clean the database by removing samples close to the
    decision boundary.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    n_neighbors : int or object, default=3
        If ``int``, size of the neighbourhood to consider to compute the
        nearest neighbors. If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the nearest-neighbors.

import numpy as np

from scipy.sparse import issparse

from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class CondensedNearestNeighbour(BaseCleaningSampler):
    """Undersample based on the condensed nearest neighbour method.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    n_neighbors : int or object, default=\
            KNeighborsClassifier(n_neighbors=1)

import numpy as np

from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, _safe_indexing

from ..base import BaseCleaningSampler
from ._tomek_links import TomekLinks
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class OneSidedSelection(BaseCleaningSampler):
    """Class to perform under-sampling based on one-sided selection method.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    n_neighbors : int or object, default=None
        If ``int``, size of the neighbourhood to consider to compute the

weights=[0.2, 0.3, 0.5],
        random_state=0,
    )
    sampler = Sampler()
    expected_stat = Counter(y)[1]
    if isinstance(sampler, BaseOverSampler):
        sampling_strategy = {2: 498, 0: 498}
        sampler.set_params(sampling_strategy=sampling_strategy)
        X_res, y_res = sampler.fit_resample(X, y)
        assert Counter(y_res)[1] == expected_stat
    elif isinstance(sampler, BaseUnderSampler):
        sampling_strategy = {2: 201, 0: 201}
        sampler.set_params(sampling_strategy=sampling_strategy)
        X_res, y_res = sampler.fit_resample(X, y)
        assert Counter(y_res)[1] == expected_stat
    elif isinstance(sampler, BaseCleaningSampler):
        sampling_strategy = [2, 0]
        sampler.set_params(sampling_strategy=sampling_strategy)
        X_res, y_res = sampler.fit_resample(X, y)
        assert Counter(y_res)[1] == expected_stat

import numpy as np
from scipy.stats import mode

from sklearn.utils import _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import check_neighbors_object
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring

SEL_KIND = ("all", "mode")


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
)
class EditedNearestNeighbours(BaseCleaningSampler):
    """Undersample based on the edited nearest neighbour method.

    This method will clean the database by removing samples close to the
    decision boundary.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    n_neighbors : int or object, default=3
        If ``int``, size of the neighbourhood to consider to compute the

from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, _safe_indexing

from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils._docstring import _n_jobs_docstring
from ...utils._docstring import _random_state_docstring


@Substitution(
    sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class CondensedNearestNeighbour(BaseCleaningSampler):
    """Undersample based on the condensed nearest neighbour method.

    Read more in the :ref:`User Guide `.

    Parameters
    ----------
    {sampling_strategy}

    {random_state}

    n_neighbors : int or object, default=\
            KNeighborsClassifier(n_neighbors=1)
        If ``int``, size of the neighbourhood to consider to compute the
        nearest neighbors. If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the nearest-neighbors.