_smote.py

"""Class to perform over-sampling using SMOTE."""

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
#          Fernando Nogueira
#          Christos Aridas
#          Dzianis Dudnik
# License: MIT

import math
from collections import Counter

import numpy as np
from scipy import sparse

from sklearn.base import clone
from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import pairwise_distances
from sklearn.preprocessing import OneHotEncoder
from sklearn.svm import SVC
from sklearn.utils import check_random_state
from sklearn.utils import _safe_indexing
from sklearn.utils import check_array
from sklearn.utils.sparsefuncs_fast import csr_mean_variance_axis0
from sklearn.utils.sparsefuncs_fast import csc_mean_variance_axis0

from .base import BaseOverSampler
from ..exceptions import raise_isinstance_error
from ..utils import check_neighbors_object
from ..utils import check_target_type
from ..utils import Substitution
from ..utils._docstring import _n_jobs_docstring
from ..utils._docstring import _random_state_docstring
from ..utils._validation import _deprecate_positional_args


class BaseSMOTE(BaseOverSampler):
    """Base class for the different SMOTE algorithms."""

    def __init__(
        self,
        sampling_strategy="auto",
        random_state=None,
        k_neighbors=5,
        n_jobs=None,
    ):
        super().__init__(sampling_strategy=sampling_strategy)
        self.random_state = random_state
        self.k_neighbors = k_neighbors
        self.n_jobs = n_jobs

    def _validate_estimator(self):
        """Check the NN estimators shared across the different SMOTE
        algorithms.
        """
        self.nn_k_ = check_neighbors_object(
            "k_neighbors", self.k_neighbors, additional_neighbor=1
        )

    def _make_samples(
        self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0
    ):
        """A support function that returns artificial samples constructed along
        the line connecting nearest neighbours.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Points from which the points will be created.

        y_dtype : dtype
            The data type of the targets.

        y_type : str or int
            The minority target value, just so the function can return the
            target values for the synthetic variables with correct length in
            a clear format.

        nn_data : ndarray of shape (n_samples_all, n_features)
            Data set carrying all the neighbours to be used

        nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours)
            The nearest neighbours of each sample in `nn_data`.

        n_samples : int
            The number of samples to generate.

        step_size : float, default=1.0
            The step size to create samples.

        Returns
        -------
        X_new : {ndarray, sparse matrix} of shape (n_samples_new, n_features)
            Synthetically generated samples.

        y_new : ndarray of shape (n_samples_new,)
            Target values for synthetic samples.
        """
        random_state = check_random_state(self.random_state)
        samples_indices = random_state.randint(
            low=0, high=nn_num.size, size=n_samples
        )

        # np.newaxis for backwards compatability with random_state
        steps = step_size * random_state.uniform(size=n_samples)[:, np.newaxis]
        rows = np.floor_divide(samples_indices, nn_num.shape[1])
        cols = np.mod(samples_indices, nn_num.shape[1])

        X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps)
        y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype)
        return X_new, y_new

    def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps):
        r"""Generate a synthetic sample.

        The rule for the generation is:

        .. math::
           \mathbf{s_{s}} = \mathbf{s_{i}} + \mathcal{u}(0, 1) \times
           (\mathbf{s_{i}} - \mathbf{s_{nn}}) \,

        where \mathbf{s_{s}} is the new synthetic samples, \mathbf{s_{i}} is
        the current sample, \mathbf{s_{nn}} is a randomly selected neighbors of
        \mathbf{s_{i}} and \mathcal{u}(0, 1) is a random number between [0, 1).

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Points from which the points will be created.

        nn_data : ndarray of shape (n_samples_all, n_features)
            Data set carrying all the neighbours to be used.

        nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours)
            The nearest neighbours of each sample in `nn_data`.

        rows : ndarray of shape (n_samples,), dtype=int
            Indices pointing at feature vector in X which will be used
            as a base for creating new samples.

        cols : ndarray of shape (n_samples,), dtype=int
            Indices pointing at which nearest neighbor of base feature vector
            will be used when creating new samples.

        steps : ndarray of shape (n_samples,), dtype=float
            Step sizes for new samples.

        Returns
        -------
        X_new : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Synthetically generated samples.
        """
        diffs = nn_data[nn_num[rows, cols]] - X[rows]

        if sparse.issparse(X):
            sparse_func = type(X).__name__
            steps = getattr(sparse, sparse_func)(steps)
            X_new = X[rows] + steps.multiply(diffs)
        else:
            X_new = X[rows] + steps * diffs

        return X_new.astype(X.dtype)

    def _in_danger_noise(
        self, nn_estimator, samples, target_class, y, kind="danger"
    ):
        """Estimate if a set of sample are in danger or noise.

        Used by BorderlineSMOTE and SVMSMOTE.

        Parameters
        ----------
        nn_estimator : estimator
            An estimator that inherits from
            :class:`sklearn.neighbors.base.KNeighborsMixin` use to determine if
            a sample is in danger/noise.

        samples : {array-like, sparse matrix} of shape (n_samples, n_features)
            The samples to check if either they are in danger or not.

        target_class : int or str
            The target corresponding class being over-sampled.

        y : array-like of shape (n_samples,)
            The true label in order to check the neighbour labels.

        kind : {'danger', 'noise'}, default='danger'
            The type of classification to use. Can be either:

            - If 'danger', check if samples are in danger,
            - If 'noise', check if samples are noise.

        Returns
        -------
        output : ndarray of shape (n_samples,)
            A boolean array where True refer to samples in danger or noise.
        """
        x = nn_estimator.kneighbors(samples, return_distance=False)[:, 1:]
        nn_label = (y[x] != target_class).astype(int)
        n_maj = np.sum(nn_label, axis=1)

        if kind == "danger":
            # Samples are in danger for m/2 <= m' < m
            return np.bitwise_and(
                n_maj >= (nn_estimator.n_neighbors - 1) / 2,
                n_maj < nn_estimator.n_neighbors - 1,
            )
        elif kind == "noise":
            # Samples are noise for m = m'
            return n_maj == nn_estimator.n_neighbors - 1
        else:
            raise NotImplementedError


@Substitution(
    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class BorderlineSMOTE(BaseSMOTE):
    """Over-sampling using Borderline SMOTE.

    This algorithm is a variant of the original SMOTE algorithm proposed in
    [2]_. Borderline samples will be detected and used to generate new
    synthetic samples.

    Read more in the :ref:`User Guide <smote_adasyn>`.

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

    {random_state}

    k_neighbors : int or object, default=5
        If ``int``, number of nearest neighbours to used to construct synthetic
        samples.  If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the k_neighbors.

    {n_jobs}

    m_neighbors : int or object, default=10
        If int, number of nearest neighbours to use to determine if a minority
        sample is in danger. If object, an estimator that inherits
        from :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used
        to find the m_neighbors.

    kind : {{"borderline-1", "borderline-2"}}, default='borderline-1'
        The type of SMOTE algorithm to use one of the following options:
        ``'borderline-1'``, ``'borderline-2'``.

    See Also
    --------
    SMOTE : Over-sample using SMOTE.

    SMOTENC : Over-sample using SMOTE for continuous and categorical features.

    SVMSMOTE : Over-sample using SVM-SMOTE variant.

    ADASYN : Over-sample using ADASYN.

    KMeansSMOTE : Over-sample applying a clustering before to oversample using
        SMOTE.

    Notes
    -----
    See the original papers: [2]_ for more details.

    Supports multi-class resampling. A one-vs.-rest scheme is used as
    originally proposed in [1]_.

    References
    ----------
    .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE:
       synthetic minority over-sampling technique," Journal of artificial
       intelligence research, 321-357, 2002.

    .. [2] H. Han, W. Wen-Yuan, M. Bing-Huan, "Borderline-SMOTE: a new
       over-sampling method in imbalanced data sets learning," Advances in
       intelligent computing, 878-887, 2005.

    Examples
    --------

    >>> from collections import Counter
    >>> from sklearn.datasets import make_classification
    >>> from imblearn.over_sampling import \
BorderlineSMOTE # doctest: +NORMALIZE_WHITESPACE
    >>> X, y = make_classification(n_classes=2, class_sep=2,
    ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
    ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
    >>> print('Original dataset shape %s' % Counter(y))
    Original dataset shape Counter({{1: 900, 0: 100}})
    >>> sm = BorderlineSMOTE(random_state=42)
    >>> X_res, y_res = sm.fit_resample(X, y)
    >>> print('Resampled dataset shape %s' % Counter(y_res))
    Resampled dataset shape Counter({{0: 900, 1: 900}})
    """

    @_deprecate_positional_args
    def __init__(
        self,
        *,
        sampling_strategy="auto",
        random_state=None,
        k_neighbors=5,
        n_jobs=None,
        m_neighbors=10,
        kind="borderline-1",
    ):
        super().__init__(
            sampling_strategy=sampling_strategy,
            random_state=random_state,
            k_neighbors=k_neighbors,
            n_jobs=n_jobs,
        )
        self.m_neighbors = m_neighbors
        self.kind = kind

    def _validate_estimator(self):
        super()._validate_estimator()
        self.nn_m_ = check_neighbors_object(
            "m_neighbors", self.m_neighbors, additional_neighbor=1
        )
        self.nn_m_.set_params(**{"n_jobs": self.n_jobs})
        if self.kind not in ("borderline-1", "borderline-2"):
            raise ValueError(
                'The possible "kind" of algorithm are '
                '"borderline-1" and "borderline-2".'
                "Got {} instead.".format(self.kind)
            )

    def _fit_resample(self, X, y):
        self._validate_estimator()

        X_resampled = X.copy()
        y_resampled = y.copy()

        for class_sample, n_samples in self.sampling_strategy_.items():
            if n_samples == 0:
                continue
            target_class_indices = np.flatnonzero(y == class_sample)
            X_class = _safe_indexing(X, target_class_indices)

            self.nn_m_.fit(X)
            danger_index = self._in_danger_noise(
                self.nn_m_, X_class, class_sample, y, kind="danger"
            )
            if not any(danger_index):
                continue

            self.nn_k_.fit(X_class)
            nns = self.nn_k_.kneighbors(
                _safe_indexing(X_class, danger_index), return_distance=False
            )[:, 1:]

            # divergence between borderline-1 and borderline-2
            if self.kind == "borderline-1":
                # Create synthetic samples for borderline points.
                X_new, y_new = self._make_samples(
                    _safe_indexing(X_class, danger_index),
                    y.dtype,
                    class_sample,
                    X_class,
                    nns,
                    n_samples,
                )
                if sparse.issparse(X_new):
                    X_resampled = sparse.vstack([X_resampled, X_new])
                else:
                    X_resampled = np.vstack((X_resampled, X_new))
                y_resampled = np.hstack((y_resampled, y_new))

            elif self.kind == "borderline-2":
                random_state = check_random_state(self.random_state)
                fractions = random_state.beta(10, 10)

                # only minority
                X_new_1, y_new_1 = self._make_samples(
                    _safe_indexing(X_class, danger_index),
                    y.dtype,
                    class_sample,
                    X_class,
                    nns,
                    int(fractions * (n_samples + 1)),
                    step_size=1.0,
                )

                # we use a one-vs-rest policy to handle the multiclass in which
                # new samples will be created considering not only the majority
                # class but all over classes.
                X_new_2, y_new_2 = self._make_samples(
                    _safe_indexing(X_class, danger_index),
                    y.dtype,
                    class_sample,
                    _safe_indexing(X, np.flatnonzero(y != class_sample)),
                    nns,
                    int((1 - fractions) * n_samples),
                    step_size=0.5,
                )

                if sparse.issparse(X_resampled):
                    X_resampled = sparse.vstack(
                        [X_resampled, X_new_1, X_new_2]
                    )
                else:
                    X_resampled = np.vstack((X_resampled, X_new_1, X_new_2))
                y_resampled = np.hstack((y_resampled, y_new_1, y_new_2))

        return X_resampled, y_resampled


@Substitution(
    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class SVMSMOTE(BaseSMOTE):
    """Over-sampling using SVM-SMOTE.

    Variant of SMOTE algorithm which use an SVM algorithm to detect sample to
    use for generating new synthetic samples as proposed in [2]_.

    Read more in the :ref:`User Guide <smote_adasyn>`.

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

    {random_state}

    k_neighbors : int or object, default=5
        If ``int``, number of nearest neighbours to used to construct synthetic
        samples.  If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the k_neighbors.

    {n_jobs}

    m_neighbors : int or object, default=10
        If int, number of nearest neighbours to use to determine if a minority
        sample is in danger. If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the m_neighbors.

    svm_estimator : object, default=SVC()
        A parametrized :class:`sklearn.svm.SVC` classifier can be passed.

    out_step : float, default=0.5
        Step size when extrapolating.

    See Also
    --------
    SMOTE : Over-sample using SMOTE.

    SMOTENC : Over-sample using SMOTE for continuous and categorical features.

    BorderlineSMOTE : Over-sample using Borderline-SMOTE.

    ADASYN : Over-sample using ADASYN.

    KMeansSMOTE : Over-sample applying a clustering before to oversample using
        SMOTE.

    Notes
    -----
    See the original papers: [2]_ for more details.

    Supports multi-class resampling. A one-vs.-rest scheme is used as
    originally proposed in [1]_.

    References
    ----------
    .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE:
       synthetic minority over-sampling technique," Journal of artificial
       intelligence research, 321-357, 2002.

    .. [2] H. M. Nguyen, E. W. Cooper, K. Kamei, "Borderline over-sampling for
       imbalanced data classification," International Journal of Knowledge
       Engineering and Soft Data Paradigms, 3(1), pp.4-21, 2009.

    Examples
    --------

    >>> from collections import Counter
    >>> from sklearn.datasets import make_classification
    >>> from imblearn.over_sampling import \
SVMSMOTE # doctest: +NORMALIZE_WHITESPACE
    >>> X, y = make_classification(n_classes=2, class_sep=2,
    ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
    ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
    >>> print('Original dataset shape %s' % Counter(y))
    Original dataset shape Counter({{1: 900, 0: 100}})
    >>> sm = SVMSMOTE(random_state=42)
    >>> X_res, y_res = sm.fit_resample(X, y)
    >>> print('Resampled dataset shape %s' % Counter(y_res))
    Resampled dataset shape Counter({{0: 900, 1: 900}})
    """

    @_deprecate_positional_args
    def __init__(
        self,
        *,
        sampling_strategy="auto",
        random_state=None,
        k_neighbors=5,
        n_jobs=None,
        m_neighbors=10,
        svm_estimator=None,
        out_step=0.5,
    ):
        super().__init__(
            sampling_strategy=sampling_strategy,
            random_state=random_state,
            k_neighbors=k_neighbors,
            n_jobs=n_jobs,
        )
        self.m_neighbors = m_neighbors
        self.svm_estimator = svm_estimator
        self.out_step = out_step

    def _validate_estimator(self):
        super()._validate_estimator()
        self.nn_m_ = check_neighbors_object(
            "m_neighbors", self.m_neighbors, additional_neighbor=1
        )
        self.nn_m_.set_params(**{"n_jobs": self.n_jobs})

        if self.svm_estimator is None:
            self.svm_estimator_ = SVC(
                gamma="scale", random_state=self.random_state
            )
        elif isinstance(self.svm_estimator, SVC):
            self.svm_estimator_ = clone(self.svm_estimator)
        else:
            raise_isinstance_error("svm_estimator", [SVC], self.svm_estimator)

    def _fit_resample(self, X, y):
        self._validate_estimator()
        random_state = check_random_state(self.random_state)
        X_resampled = X.copy()
        y_resampled = y.copy()

        for class_sample, n_samples in self.sampling_strategy_.items():
            if n_samples == 0:
                continue
            target_class_indices = np.flatnonzero(y == class_sample)
            X_class = _safe_indexing(X, target_class_indices)

            self.svm_estimator_.fit(X, y)
            support_index = self.svm_estimator_.support_[
                y[self.svm_estimator_.support_] == class_sample
            ]
            support_vector = _safe_indexing(X, support_index)

            self.nn_m_.fit(X)
            
            prev_support_vector = support_vector
            
            noise_bool = self._in_danger_noise(
                self.nn_m_, support_vector, class_sample, y, kind="noise"
            )
            support_vector = _safe_indexing(
                support_vector, np.flatnonzero(np.logical_not(noise_bool))
            )
            
            if len(support_vector) == 0:
                support_vector = prev_support_vector
            
            danger_bool = self._in_danger_noise(
                self.nn_m_, support_vector, class_sample, y, kind="danger"
            )
            safety_bool = np.logical_not(danger_bool)

            self.nn_k_.fit(X_class)
            fractions = random_state.beta(10, 10)
            n_generated_samples = int(fractions * (n_samples + 1))
            if np.count_nonzero(danger_bool) > 0:
                nns = self.nn_k_.kneighbors(
                    _safe_indexing(support_vector, np.flatnonzero(danger_bool)),
                    return_distance=False,
                )[:, 1:]

                X_new_1, y_new_1 = self._make_samples(
                    _safe_indexing(support_vector, np.flatnonzero(danger_bool)),
                    y.dtype,
                    class_sample,
                    X_class,
                    nns,
                    n_generated_samples,
                    step_size=1.0,
                )

            if np.count_nonzero(safety_bool) > 0:
                nns = self.nn_k_.kneighbors(
                    _safe_indexing(support_vector, np.flatnonzero(safety_bool)),
                    return_distance=False,
                )[:, 1:]

                X_new_2, y_new_2 = self._make_samples(
                    _safe_indexing(support_vector, np.flatnonzero(safety_bool)),
                    y.dtype,
                    class_sample,
                    X_class,
                    nns,
                    n_samples - n_generated_samples,
                    step_size=-self.out_step,
                )

            if (
                np.count_nonzero(danger_bool) > 0
                and np.count_nonzero(safety_bool) > 0
            ):
                if sparse.issparse(X_resampled):
                    X_resampled = sparse.vstack(
                        [X_resampled, X_new_1, X_new_2]
                    )
                else:
                    X_resampled = np.vstack((X_resampled, X_new_1, X_new_2))
                y_resampled = np.concatenate(
                    (y_resampled, y_new_1, y_new_2), axis=0
                )
            elif np.count_nonzero(danger_bool) == 0:
                if sparse.issparse(X_resampled):
                    X_resampled = sparse.vstack([X_resampled, X_new_2])
                else:
                    X_resampled = np.vstack((X_resampled, X_new_2))
                y_resampled = np.concatenate((y_resampled, y_new_2), axis=0)
            elif np.count_nonzero(safety_bool) == 0:
                if sparse.issparse(X_resampled):
                    X_resampled = sparse.vstack([X_resampled, X_new_1])
                else:
                    X_resampled = np.vstack((X_resampled, X_new_1))
                y_resampled = np.concatenate((y_resampled, y_new_1), axis=0)

        return X_resampled, y_resampled


@Substitution(
    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class SMOTE(BaseSMOTE):
    """Class to perform over-sampling using SMOTE.

    This object is an implementation of SMOTE - Synthetic Minority
    Over-sampling Technique as presented in [1]_.

    Read more in the :ref:`User Guide <smote_adasyn>`.

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

    {random_state}

    k_neighbors : int or object, default=5
        If ``int``, number of nearest neighbours to used to construct synthetic
        samples.  If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the k_neighbors.

    {n_jobs}

    See Also
    --------
    SMOTENC : Over-sample using SMOTE for continuous and categorical features.

    BorderlineSMOTE : Over-sample using the borderline-SMOTE variant.

    SVMSMOTE : Over-sample using the SVM-SMOTE variant.

    ADASYN : Over-sample using ADASYN.

    KMeansSMOTE : Over-sample applying a clustering before to oversample using
        SMOTE.

    Notes
    -----
    See the original papers: [1]_ for more details.

    Supports multi-class resampling. A one-vs.-rest scheme is used as
    originally proposed in [1]_.

    References
    ----------
    .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE:
       synthetic minority over-sampling technique," Journal of artificial
       intelligence research, 321-357, 2002.

    Examples
    --------

    >>> from collections import Counter
    >>> from sklearn.datasets import make_classification
    >>> from imblearn.over_sampling import \
SMOTE # doctest: +NORMALIZE_WHITESPACE
    >>> X, y = make_classification(n_classes=2, class_sep=2,
    ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
    ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
    >>> print('Original dataset shape %s' % Counter(y))
    Original dataset shape Counter({{1: 900, 0: 100}})
    >>> sm = SMOTE(random_state=42)
    >>> X_res, y_res = sm.fit_resample(X, y)
    >>> print('Resampled dataset shape %s' % Counter(y_res))
    Resampled dataset shape Counter({{0: 900, 1: 900}})
    """

    @_deprecate_positional_args
    def __init__(
        self,
        *,
        sampling_strategy="auto",
        random_state=None,
        k_neighbors=5,
        n_jobs=None,
    ):
        super().__init__(
            sampling_strategy=sampling_strategy,
            random_state=random_state,
            k_neighbors=k_neighbors,
            n_jobs=n_jobs,
        )

    def _fit_resample(self, X, y):
        self._validate_estimator()

        X_resampled = [X.copy()]
        y_resampled = [y.copy()]

        for class_sample, n_samples in self.sampling_strategy_.items():
            if n_samples == 0:
                continue
            target_class_indices = np.flatnonzero(y == class_sample)
            X_class = _safe_indexing(X, target_class_indices)

            self.nn_k_.fit(X_class)
            nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:]
            X_new, y_new = self._make_samples(
                X_class, y.dtype, class_sample, X_class, nns, n_samples, 1.0
            )
            X_resampled.append(X_new)
            y_resampled.append(y_new)

        if sparse.issparse(X):
            X_resampled = sparse.vstack(X_resampled, format=X.format)
        else:
            X_resampled = np.vstack(X_resampled)
        y_resampled = np.hstack(y_resampled)

        return X_resampled, y_resampled


# @Substitution(
#     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
#     random_state=_random_state_docstring)
class SMOTENC(SMOTE):
    """Synthetic Minority Over-sampling Technique for Nominal and Continuous.

    Unlike :class:`SMOTE`, SMOTE-NC for dataset containing continuous and
    categorical features. However, it is not designed to work with only
    categorical features.

    Read more in the :ref:`User Guide <smote_adasyn>`.

    Parameters
    ----------
    categorical_features : ndarray of shape (n_cat_features,) or (n_features,)
        Specified which features are categorical. Can either be:

        - array of indices specifying the categorical features;
        - mask array of shape (n_features, ) and ``bool`` dtype for which
          ``True`` indicates the categorical features.

    sampling_strategy : float, str, dict or callable, default='auto'
        Sampling information to resample the data set.

        - When ``float``, it corresponds to the desired ratio of the number of
          samples in the minority class over the number of samples in the
          majority class after resampling. Therefore, the ratio is expressed as
          :math:`\\alpha_{os} = N_{rm} / N_{M}` where :math:`N_{rm}` is the
          number of samples in the minority class after resampling and
          :math:`N_{M}` is the number of samples in the majority class.

            .. warning::
               ``float`` is only available for **binary** classification. An
               error is raised for multi-class classification.

        - When ``str``, specify the class targeted by the resampling. The
          number of samples in the different classes will be equalized.
          Possible choices are:

            ``'minority'``: resample only the minority class;

            ``'not minority'``: resample all classes but the minority class;

            ``'not majority'``: resample all classes but the majority class;

            ``'all'``: resample all classes;

            ``'auto'``: equivalent to ``'not majority'``.

        - When ``dict``, the keys correspond to the targeted classes. The
          values correspond to the desired number of samples for each targeted
          class.

        - When callable, function taking ``y`` and returns a ``dict``. The keys
          correspond to the targeted classes. The values correspond to the
          desired number of samples for each class.

    random_state : int, RandomState instance, default=None
        Control the randomization of the algorithm.

        - If int, ``random_state`` is the seed used by the random number
          generator;
        - If ``RandomState`` instance, random_state is the random number
          generator;
        - If ``None``, the random number generator is the ``RandomState``
          instance used by ``np.random``.

    k_neighbors : int or object, default=5
        If ``int``, number of nearest neighbours to used to construct synthetic
        samples.  If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the k_neighbors.

    n_jobs : int, default=None
        Number of CPU cores used during the cross-validation loop.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See
        `Glossary <https://scikit-learn.org/stable/glossary.html#term-n-jobs>`_
        for more details.

    See Also
    --------
    SMOTE : Over-sample using SMOTE.

    SVMSMOTE : Over-sample using SVM-SMOTE variant.

    BorderlineSMOTE : Over-sample using Borderline-SMOTE variant.

    ADASYN : Over-sample using ADASYN.

    KMeansSMOTE : Over-sample applying a clustering before to oversample using
        SMOTE.

    Notes
    -----
    See the original paper [1]_ for more details.

    Supports mutli-class resampling. A one-vs.-rest scheme is used as
    originally proposed in [1]_.

    See
    :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py`,
    and :ref:`sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py`.

    References
    ----------
    .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE:
       synthetic minority over-sampling technique," Journal of artificial
       intelligence research, 321-357, 2002.

    Examples
    --------

    >>> from collections import Counter
    >>> from numpy.random import RandomState
    >>> from sklearn.datasets import make_classification
    >>> from imblearn.over_sampling import SMOTENC
    >>> X, y = make_classification(n_classes=2, class_sep=2,
    ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
    ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
    >>> print('Original dataset shape (%s, %s)' % X.shape)
    Original dataset shape (1000, 20)
    >>> print('Original dataset samples per class {}'.format(Counter(y)))
    Original dataset samples per class Counter({1: 900, 0: 100})
    >>> # simulate the 2 last columns to be categorical features
    >>> X[:, -2:] = RandomState(10).randint(0, 4, size=(1000, 2))
    >>> sm = SMOTENC(random_state=42, categorical_features=[18, 19])
    >>> X_res, y_res = sm.fit_resample(X, y)
    >>> print('Resampled dataset samples per class {}'.format(Counter(y_res)))
    Resampled dataset samples per class Counter({0: 900, 1: 900})
    """

    _required_parameters = ["categorical_features"]

    @_deprecate_positional_args
    def __init__(
        self,
        categorical_features,
        *,
        sampling_strategy="auto",
        random_state=None,
        k_neighbors=5,
        n_jobs=None,
    ):
        super().__init__(
            sampling_strategy=sampling_strategy,
            random_state=random_state,
            k_neighbors=k_neighbors,
        )
        self.categorical_features = categorical_features

    def _check_X_y(self, X, y):
        """Overwrite the checking to let pass some string for categorical
        features.
        """
        y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
        X, y = self._validate_data(
            X, y, reset=True, dtype=None, accept_sparse=["csr", "csc"]
        )
        return X, y, binarize_y

    def _validate_estimator(self):
        super()._validate_estimator()
        categorical_features = np.asarray(self.categorical_features)
        if categorical_features.dtype.name == "bool":
            self.categorical_features_ = np.flatnonzero(categorical_features)
        else:
            if any(
                [
                    cat not in np.arange(self.n_features_)
                    for cat in categorical_features
                ]
            ):
                raise ValueError(
                    "Some of the categorical indices are out of range. Indices"
                    " should be between 0 and {}".format(self.n_features_)
                )
            self.categorical_features_ = categorical_features
        self.continuous_features_ = np.setdiff1d(
            np.arange(self.n_features_), self.categorical_features_
        )

        if self.categorical_features_.size == self.n_features_in_:
            raise ValueError(
                "SMOTE-NC is not designed to work only with categorical "
                "features. It requires some numerical features."
            )

    def _fit_resample(self, X, y):
        self.n_features_ = X.shape[1]
        self._validate_estimator()

        # compute the median of the standard deviation of the minority class
        target_stats = Counter(y)
        class_minority = min(target_stats, key=target_stats.get)

        X_continuous = X[:, self.continuous_features_]
        X_continuous = check_array(X_continuous, accept_sparse=["csr", "csc"])
        X_minority = _safe_indexing(
            X_continuous, np.flatnonzero(y == class_minority)
        )

        if sparse.issparse(X):
            if X.format == "csr":
                _, var = csr_mean_variance_axis0(X_minority)
            else:
                _, var = csc_mean_variance_axis0(X_minority)
        else:
            var = X_minority.var(axis=0)
        self.median_std_ = np.median(np.sqrt(var))

        X_categorical = X[:, self.categorical_features_]
        if X_continuous.dtype.name != "object":
            dtype_ohe = X_continuous.dtype
        else:
            dtype_ohe = np.float64
        self.ohe_ = OneHotEncoder(
            sparse=True, handle_unknown="ignore", dtype=dtype_ohe
        )

        # the input of the OneHotEncoder needs to be dense
        X_ohe = self.ohe_.fit_transform(
            X_categorical.toarray()
            if sparse.issparse(X_categorical)
            else X_categorical
        )

        # we can replace the 1 entries of the categorical features with the
        # median of the standard deviation. It will ensure that whenever
        # distance is computed between 2 samples, the difference will be equal
        # to the median of the standard deviation as in the original paper.

        # In the edge case where the median of the std is equal to 0, the 1s
        # entries will be also nullified. In this case, we store the original
        # categorical encoding which will be later used for inversing the OHE
        if math.isclose(self.median_std_, 0):
            self._X_categorical_minority_encoded = _safe_indexing(
                X_ohe.toarray(), np.flatnonzero(y == class_minority)
            )

        X_ohe.data = (
            np.ones_like(X_ohe.data, dtype=X_ohe.dtype) * self.median_std_ / 2
        )
        X_encoded = sparse.hstack((X_continuous, X_ohe), format="csr")

        X_resampled, y_resampled = super()._fit_resample(X_encoded, y)

        # reverse the encoding of the categorical features
        X_res_cat = X_resampled[:, self.continuous_features_.size:]
        X_res_cat.data = np.ones_like(X_res_cat.data)
        X_res_cat_dec = self.ohe_.inverse_transform(X_res_cat)

        if sparse.issparse(X):
            X_resampled = sparse.hstack(
                (
                    X_resampled[:, : self.continuous_features_.size],
                    X_res_cat_dec,
                ),
                format="csr",
            )
        else:
            X_resampled = np.hstack(
                (
                    X_resampled[:, : self.continuous_features_.size].toarray(),
                    X_res_cat_dec,
                )
            )

        indices_reordered = np.argsort(
            np.hstack((self.continuous_features_, self.categorical_features_))
        )
        if sparse.issparse(X_resampled):
            # the matrix is supposed to be in the CSR format after the stacking
            col_indices = X_resampled.indices.copy()
            for idx, col_idx in enumerate(indices_reordered):
                mask = X_resampled.indices == col_idx
                col_indices[mask] = idx
            X_resampled.indices = col_indices
        else:
            X_resampled = X_resampled[:, indices_reordered]

        return X_resampled, y_resampled

    def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps):
        """Generate a synthetic sample with an additional steps for the
        categorical features.

        Each new sample is generated the same way than in SMOTE. However, the
        categorical features are mapped to the most frequent nearest neighbors
        of the majority class.
        """
        rng = check_random_state(self.random_state)
        X_new = super()._generate_samples(
            X, nn_data, nn_num, rows, cols, steps
        )
        # change in sparsity structure more efficient with LIL than CSR
        X_new = (X_new.tolil() if sparse.issparse(X_new) else X_new)

        # convert to dense array since scipy.sparse doesn't handle 3D
        nn_data = (nn_data.toarray() if sparse.issparse(nn_data) else nn_data)

        # In the case that the median std was equal to zeros, we have to
        # create non-null entry based on the encoded of OHE
        if math.isclose(self.median_std_, 0):
            nn_data[:, self.continuous_features_.size:] = (
                self._X_categorical_minority_encoded
            )

        all_neighbors = nn_data[nn_num[rows]]

        categories_size = [self.continuous_features_.size] + [
            cat.size for cat in self.ohe_.categories_
        ]

        for start_idx, end_idx in zip(np.cumsum(categories_size)[:-1],
                                      np.cumsum(categories_size)[1:]):
            col_maxs = all_neighbors[:, :, start_idx:end_idx].sum(axis=1)
            # tie breaking argmax
            is_max = np.isclose(col_maxs, col_maxs.max(axis=1, keepdims=True))
            max_idxs = rng.permutation(np.argwhere(is_max))
            xs, idx_sels = np.unique(max_idxs[:, 0], return_index=True)
            col_sels = max_idxs[idx_sels, 1]

            ys = start_idx + col_sels
            X_new[:, start_idx:end_idx] = 0
            X_new[xs, ys] = 1

        return X_new


@Substitution(
    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
    n_jobs=_n_jobs_docstring,
    random_state=_random_state_docstring,
)
class KMeansSMOTE(BaseSMOTE):
    """Apply a KMeans clustering before to over-sample using SMOTE.

    This is an implementation of the algorithm described in [1]_.

    Read more in the :ref:`User Guide <smote_adasyn>`.

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

    {random_state}

    k_neighbors : int or object, default=2
        If ``int``, number of nearest neighbours to used to construct synthetic
        samples.  If object, an estimator that inherits from
        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
        find the k_neighbors.

    {n_jobs}

    kmeans_estimator : int or object, default=None
        A KMeans instance or the number of clusters to be used. By default,
        we used a :class:`sklearn.cluster.MiniBatchKMeans` which tend to be
        better with large number of samples.

    cluster_balance_threshold : "auto" or float, default="auto"
        The threshold at which a cluster is called balanced and where samples
        of the class selected for SMOTE will be oversampled. If "auto", this
        will be determined by the ratio for each class, or it can be set
        manually.

    density_exponent : "auto" or float, default="auto"
        This exponent is used to determine the density of a cluster. Leaving
        this to "auto" will use a feature-length based exponent.

    Attributes
    ----------
    kmeans_estimator_ : estimator
        The fitted clustering method used before to apply SMOTE.

    nn_k_ : estimator
        The fitted k-NN estimator used in SMOTE.

    cluster_balance_threshold_ : float
        The threshold used during ``fit`` for calling a cluster balanced.

    See Also
    --------
    SMOTE : Over-sample using SMOTE.

    SVMSMOTE : Over-sample using SVM-SMOTE variant.

    BorderlineSMOTE : Over-sample using Borderline-SMOTE variant.

    ADASYN : Over-sample using ADASYN.

    References
    ----------
    .. [1] Felix Last, Georgios Douzas, Fernando Bacao, "Oversampling for
       Imbalanced Learning Based on K-Means and SMOTE"
       https://arxiv.org/abs/1711.00837

    Examples
    --------

    >>> import numpy as np
    >>> from imblearn.over_sampling import KMeansSMOTE
    >>> from sklearn.datasets import make_blobs
    >>> blobs = [100, 800, 100]
    >>> X, y  = make_blobs(blobs, centers=[(-10, 0), (0,0), (10, 0)])
    >>> # Add a single 0 sample in the middle blob
    >>> X = np.concatenate([X, [[0, 0]]])
    >>> y = np.append(y, 0)
    >>> # Make this a binary classification problem
    >>> y = y == 1
    >>> sm = KMeansSMOTE(random_state=42)
    >>> X_res, y_res = sm.fit_resample(X, y)
    >>> # Find the number of new samples in the middle blob
    >>> n_res_in_middle = ((X_res[:, 0] > -5) & (X_res[:, 0] < 5)).sum()
    >>> print("Samples in the middle blob: %s" % n_res_in_middle)
    Samples in the middle blob: 801
    >>> print("Middle blob unchanged: %s" % (n_res_in_middle == blobs[1] + 1))
    Middle blob unchanged: True
    >>> print("More 0 samples: %s" % ((y_res == 0).sum() > (y == 0).sum()))
    More 0 samples: True
    """

    @_deprecate_positional_args
    def __init__(
        self,
        *,
        sampling_strategy="auto",
        random_state=None,
        k_neighbors=2,
        n_jobs=None,
        kmeans_estimator=None,
        cluster_balance_threshold="auto",
        density_exponent="auto",
    ):
        super().__init__(
            sampling_strategy=sampling_strategy,
            random_state=random_state,
            k_neighbors=k_neighbors,
            n_jobs=n_jobs,
        )
        self.kmeans_estimator = kmeans_estimator
        self.cluster_balance_threshold = cluster_balance_threshold
        self.density_exponent = density_exponent

    def _validate_estimator(self):
        super()._validate_estimator()
        if self.kmeans_estimator is None:
            self.kmeans_estimator_ = MiniBatchKMeans(
                random_state=self.random_state
            )
        elif isinstance(self.kmeans_estimator, int):
            self.kmeans_estimator_ = MiniBatchKMeans(
                n_clusters=self.kmeans_estimator,
                random_state=self.random_state,
            )
        else:
            self.kmeans_estimator_ = clone(self.kmeans_estimator)

        # validate the parameters
        for param_name in ("cluster_balance_threshold", "density_exponent"):
            param = getattr(self, param_name)
            if isinstance(param, str) and param != "auto":
                raise ValueError(
                    "'{}' should be 'auto' when a string is passed. "
                    "Got {} instead.".format(param_name, repr(param))
                )

        self.cluster_balance_threshold_ = (
            self.cluster_balance_threshold
            if self.kmeans_estimator_.n_clusters != 1
            else -np.inf
        )

    def _find_cluster_sparsity(self, X):
        """Compute the cluster sparsity."""
        euclidean_distances = pairwise_distances(
            X, metric="euclidean", n_jobs=self.n_jobs
        )
        # negate diagonal elements
        for ind in range(X.shape[0]):
            euclidean_distances[ind, ind] = 0

        non_diag_elements = (X.shape[0] ** 2) - X.shape[0]
        mean_distance = euclidean_distances.sum() / non_diag_elements
        exponent = (
            math.log(X.shape[0], 1.6) ** 1.8 * 0.16
            if self.density_exponent == "auto"
            else self.density_exponent
        )
        return (mean_distance ** exponent) / X.shape[0]

    def _fit_resample(self, X, y):
        self._validate_estimator()
        X_resampled = X.copy()
        y_resampled = y.copy()
        total_inp_samples = sum(self.sampling_strategy_.values())

        for class_sample, n_samples in self.sampling_strategy_.items():
            if n_samples == 0:
                continue

            # target_class_indices = np.flatnonzero(y == class_sample)
            # X_class = _safe_indexing(X, target_class_indices)

            X_clusters = self.kmeans_estimator_.fit_predict(X)
            valid_clusters = []
            cluster_sparsities = []

            # identify cluster which are answering the requirements
            for cluster_idx in range(self.kmeans_estimator_.n_clusters):

                cluster_mask = np.flatnonzero(X_clusters == cluster_idx)
                X_cluster = _safe_indexing(X, cluster_mask)
                y_cluster = _safe_indexing(y, cluster_mask)

                cluster_class_mean = (y_cluster == class_sample).mean()

                if self.cluster_balance_threshold_ == "auto":
                    balance_threshold = n_samples / total_inp_samples / 2
                else:
                    balance_threshold = self.cluster_balance_threshold_

                # the cluster is already considered balanced
                if cluster_class_mean < balance_threshold:
                    continue

                # not enough samples to apply SMOTE
                anticipated_samples = cluster_class_mean * X_cluster.shape[0]
                if anticipated_samples < self.nn_k_.n_neighbors:
                    continue

                X_cluster_class = _safe_indexing(
                    X_cluster, np.flatnonzero(y_cluster == class_sample)
                )

                valid_clusters.append(cluster_mask)
                cluster_sparsities.append(
                    self._find_cluster_sparsity(X_cluster_class)
                )

            cluster_sparsities = np.array(cluster_sparsities)
            cluster_weights = cluster_sparsities / cluster_sparsities.sum()

            if not valid_clusters:
                raise RuntimeError(
                    "No clusters found with sufficient samples of "
                    "class {}. Try lowering the cluster_balance_threshold "
                    "or increasing the number of "
                    "clusters.".format(class_sample)
                )

            for valid_cluster_idx, valid_cluster in enumerate(valid_clusters):
                X_cluster = _safe_indexing(X, valid_cluster)
                y_cluster = _safe_indexing(y, valid_cluster)

                X_cluster_class = _safe_indexing(
                    X_cluster, np.flatnonzero(y_cluster == class_sample)
                )

                self.nn_k_.fit(X_cluster_class)
                nns = self.nn_k_.kneighbors(
                    X_cluster_class, return_distance=False
                )[:, 1:]

                cluster_n_samples = int(
                    math.ceil(n_samples * cluster_weights[valid_cluster_idx])
                )

                X_new, y_new = self._make_samples(
                    X_cluster_class,
                    y.dtype,
                    class_sample,
                    X_cluster_class,
                    nns,
                    cluster_n_samples,
                    1.0,
                )

                stack = [np.vstack, sparse.vstack][int(sparse.issparse(X_new))]
                X_resampled = stack((X_resampled, X_new))
                y_resampled = np.hstack((y_resampled, y_new))

        return X_resampled, y_resampled